[ { "id": "ohrhallinger-2025-tsd", "type_id": "talk", "tu_id": null, "repositum_id": "20.500.12708/213240", "title": "The Sampling-Reconstruction Dual", "date": "2025-02", "abstract": "Reconstructing surfaces of the real world from scans is an important and challenging problem. Its feasibility is limited by the number of the acquired points and their geometric configuration. The question of how many points exactly are required for the faithful reconstruction of the features leads to its inverse problem, sampling a known surface with the least possible number of points.\r\n \r\nThis talk is about reconstruction algorithms and attempts to prove their theoretical bounds in the number of points required and its dual, sampling curves (as their simpler 2D equivalent) and surfaces with specified bounds from different representations such as meshes, smooth higher-order boundaries, subdivision limit surfaces, and signed distance functions, depending on the application, e.g., lossless reduction of scanned data size, measuring scan error, handling scan artifacts such as noise, outliers, and holes, or secondary goals such as accelerating simulations.\r\n \r\nThe underlying assumption is that the smooth surface (reconstructed, or sampled) is richer than the sparse discrete set of geometric primitives (points + connectivity) it is represented with, leading to the goal of representing object boundaries, e.g., from the physical world, with the least amount of geometry.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], "publication_field": [ { "publ_id": "antes-2026-maa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "ausserer-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "balvan-rsf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "beleznai_csaba-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "belic_dejan_pr", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bodzenta-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "brenner_simon-2013-ba", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "csecsy-fcd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "darovskikh_leonid-2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "deak-a3", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "depner_dennis-2020-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "depner_dennis-2022-maa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "elagrod_ahmed-baa", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "fitzinger_andreas-daa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "fraissl_niklas-2023-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "fuchs_peter-2024-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "fuericht_2025_prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "fuericht_elias-2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "geyer_lukas_2015_prj", "field_id": "supervisor", "ordernum": 0 }, 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1 }, { "publ_id": "wiesinger_klemens-2023-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wildt-tci", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "withalm_brigitte-2022-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wolffhardt-2026-prj", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "yoo-chaeran-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "zeilinger_matthias-2020-baa", "field_id": "co_supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=948", "__class": "Author" } ], "date_from": "2025-01-13", "date_to": "2025-02-14", "event": "Infinite-dimensional Geometry: Theory and Applications", "lecturer": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], "publication_field": [ { "publ_id": "antes-2026-maa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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The design and editing of curves on surfaces has only recently begun to receive attention, primarily relying on human assistance, and where not, limited by very strict sampling conditions. In this work, we formally improve on the state-of-the-art requirements and introduce an innovative algorithm capable of reconstructing closed curves directly on surfaces from a given sparse set of sample points. We extend and adapt a state-of-the-art planar curve reconstruction method to the realm of surfaces while dealing with the challenges arising from working on non-Euclidean domains. We demonstrate the robustness of our method by reconstructing multiple curves on various surface meshes. 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-rcf/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2024-inv", "type_id": "talk", "tu_id": null, "repositum_id": null, "title": "Sampling and reconstructing point clouds", "date": "2024-05-28", "abstract": "Curve and surface reconstruction from unstructured points represent a fundamental problem in computer graphics and computer vision, with many applications. The quest for better solutions for this ill-posed problem is riddled with various kinds of artifacts such as noise, outliers, and missing data.\n\nMoreover, the reconstruction problem usually implies further input requirements: how many samples do we need for a successful reconstruction, what properties should these samples satisfy and how can we obtain such sets. And once we obtain these point samples, how can we extract connectivity that best approximates the initial surface they have been sampled from?\n\nWe will discuss about various sampling strategies, corresponding reconstruction methods, with multiple applications in automating sketch coloring, adaptive meshing for faster simulations, and cultural heritage.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1310, "image_height": 596, "name": "ohrhallinger_stefan-2024-inv-image.png", "type": "image/png", "size": 762834, "path": "Publication:ohrhallinger_stefan-2024-inv", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ohrhallinger_stefan-2024-inv/ohrhallinger_stefan-2024-inv-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ohrhallinger_stefan-2024-inv/ohrhallinger_stefan-2024-inv-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], "publication_field": [ { "publ_id": "antes-2026-maa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "ausserer-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "balvan-rsf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "beleznai_csaba-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "belic_dejan_pr", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bodzenta-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "brenner_simon-2013-ba", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "csecsy-fcd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "darovskikh_leonid-2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "deak-a3", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "depner_dennis-2020-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "depner_dennis-2022-maa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "elagrod_ahmed-baa", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "fitzinger_andreas-daa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "fraissl_niklas-2023-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "fuchs_peter-2024-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "fuericht_2025_prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "fuericht_elias-2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "geyer_lukas_2015_prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": 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1 }, { "publ_id": "wiesinger_klemens-2023-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wildt-tci", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "withalm_brigitte-2022-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wolffhardt-2026-prj", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "yoo-chaeran-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "zeilinger_matthias-2020-baa", "field_id": "co_supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=948", "__class": "Author" }, { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": "https://www.cg.tuwien.ac.at/staff/DianaMarin", "firstname": "Diana", "lastname": "Marin", "searchname": "Diana Marin", "is_teacher": false, "name": "Diana Marin", "publications": [ "ehlers-2024-vgs", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "marin-thesis", "ohrhallinger_stefan-2024-inv" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Komon2022", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sig", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sigdt", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2023-pic", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-dsr", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-pcp", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-rcf", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-sing", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "mujadzic-2023-hfp", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1848", "__class": "Author" } ], "event": "TU Graz", "location": "Graz", "research_areas": [ { "id": "Geometry", "drupal_id": 4129, "name": "Geometry Processing", "short_abstract": "Uses concepts from applied mathematics and computer science to design efficient algorithms for the reconstruction, analysis, manipulation, simulation and transmission of complex 3D models. Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. ", "website": "https://www.cg.tuwien.ac.at/research/area/Geometry", "teaser": { "name": "teaser.png", "path": "research_area:Geometry", "type": "image/png", "size": 168072, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Geometry/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "aichner-2016-sadf", "arikan-2013-osn", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "brunner_lukas_2022_ssb", "celarek-2022-gmcn", "clemenz_2019_rosy_fields", "cornel-2022-wih", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "Doerflinger-2016-CDV", "elagrod_ahmed-baa", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fitzinger_andreas-daa", "forsythe-2016-ccm", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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We adapt a surface reconstruction method to work in a distributed fashion on a high-performance cluster, reconstructing datasets with millions of vertices in seconds by exploiting the locality of the connectivity required by the reconstruction algorithm to efficiently divide-and-conquer the problem of creating triangulations from very large unstructured point clouds.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2017, "image_height": 1227, "name": "marin-2024-dsr-image.png", "type": "image/png", "size": 3376780, "path": "Publication:marin-2024-dsr", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-image:thumb{{size}}.png" }, 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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In this paper, we analyze an alternative to the often-used k-nearest neighborhood (kNN) graph - the Spheres of Influence Graph (SIG). We show that the edges that are neighboring each vertex are spatially bounded, which allows for fast computation of SIG. Our approach shows a better encoding of the ground truth connectivity compared to the kNN for a wide range of k, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., parameter-free normal estimation, and consequently, surface reconstruction, motion planning, simulations, and many more.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 742, "image_height": 912, "name": "marin-2024-pcp-image.jpg", "type": "image/jpeg", "size": 190057, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": 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Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. 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"tumpach_barbara-pra", "unterguggenberger-2021-msh", "Vucenovic_2022", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "Wimmer-1998-SMO", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2016-HARVEST4D", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa", "Zezulka-2024-FTS" ], "projects": [ "3DSpatialization", "d4388", "d9275", "EVOCATION", "GameTools", "Harvest4D", "SCI_BIM", "ShapeAcquisition", "Superhumans", "TERAPOINTS", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry", "__class": "ResearchArea" } ], "keywords": [ "Proximity Graphs", "Point Clouds", "Connectivity" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 742, "image_height": 912, "name": "marin-2024-pcp-image.jpg", "type": "image/jpeg", "size": 190057, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-2024-pcp-paper.pdf", "type": "application/pdf", "size": 2193223, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "Superhumans", "workgroup_id": "rend", "drupal_id": 4166, "drupal_path": "/research/projects/Superhumans", "name": "Superhumans - Walking Through Walls", "name_de": null, "short_title": "Superhumans", "website": null, "status": "active", "short_abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/", "__class": "Publication" }, { "id": "parakkat-2024-ballmerge", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/197864", "title": "BallMerge: High‐quality Fast Surface Reconstruction via Voronoi Balls", "date": "2024", "abstract": "We introduce a Delaunay-based algorithm for reconstructing the underlying surface of a given set of unstructured points in 3D. The implementation is very simple, and it is designed to work in a parameter-free manner. The solution builds upon the fact that in the continuous case, a closed surface separates the set of maximal empty balls (medial balls) into an interior and exterior. Based on discrete input samples, our reconstructed surface consists of the interface between Voronoi balls, which approximate the interior and exterior medial balls. An initial set of Voronoi balls is iteratively processed, merging Voronoi-ball pairs if they fulfil an overlapping error criterion. Our complete open-source reconstruction pipeline performs up to two quick linear-time passes on the Delaunay complex to output the surface, making it an order of magnitude faster than the state of the art while being competitive in memory usage and often superior in quality. We propose two variants (local and global), which are carefully designed to target two different reconstruction scenarios for watertight surfaces from accurate or noisy samples, as well as real-world scanned data sets, exhibiting noise, outliers, and large areas of missing data. The results of the global variant are, by definition, watertight, suitable for numerical analysis and various applications (e.g., 3D printing). Compared to classical Delaunay-based reconstruction techniques, our method is highly stable and robust to noise and outliers, evidenced via various experiments, including on real-world data with challenges such as scan shadows, outliers, and noise, even without additional preprocessing.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 813, "image_height": 248, "name": "parakkat-2024-ballmerge-image.png", "type": "image/png", "size": 308382, "path": "Publication:parakkat-2024-ballmerge", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/parakkat-2024-ballmerge/parakkat-2024-ballmerge-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/parakkat-2024-ballmerge/parakkat-2024-ballmerge-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1825, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Amal Dev", "lastname": "Parakkat", "searchname": "Amal Dev Parakkat", "is_teacher": false, "name": "Amal Dev Parakkat", "publications": [ "marin-2024-sing", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "parakkat-2024-ballmerge" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "cardoso-thesis", "field_id": "reviewer_1", "ordernum": 0 }, { "publ_id": "ohrhallinger-2021-egs", "field_id": "lecturer", "ordernum": 2 }, { "publ_id": "ohrhallinger-2022-e2t", "field_id": "lecturer", "ordernum": 1 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1825", "__class": "Author" }, { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], 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"https://www.cg.tuwien.ac.at/research/publications/2024/parakkat-2024-ballmerge/parakkat-2024-ballmerge-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "Superhumans", "workgroup_id": "rend", "drupal_id": 4166, "drupal_path": "/research/projects/Superhumans", "name": "Superhumans - Walking Through Walls", "name_de": null, "short_title": "Superhumans", "website": null, "status": "active", "short_abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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Following the footsteps of ε-sampling, which was originally proposed in the context of curve reconstruction to offer provable topological guarantees [ABE98] under quantifiable bounds, we propose an arbitrarily precise ε-sampling algorithm for sampling smooth planar curves (with a prior bound on the minimum feature size of the curve). This paper not only introduces the first such algorithm which provides user-control and quantifiable precision but also highlights the importance of such a sampling process under two key contexts: 1) To conduct a first study comparing theoretical sampling conditions with practical sampling requirements for reconstruction guarantees that can further be used for analysing the upper bounds of ε for various reconstruction algorithms with or without proofs, 2) As a feature-aware sampling of vector line art that can be used for applications such as coloring and meshing. 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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In this poster, we propose an extension to the proximity graph introduced in [MOW22] to three-dimensional models. We use the spheres-of-influence (SIG) proximity graph restricted to the 3D Delaunay graph to compute connectivity between points. Our approach shows a better encoding of the connectivity in relation to the ground truth than the k-nearest neighborhood (kNN) for a wide range of k values, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., improvements in normal estimation, surface reconstruction, motion planning, simulations, and many more.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "hidden", "image_width": 455, "image_height": 200, "name": "marin-2023-pic-image.png", "type": "image/png", "size": 20480, "path": "Publication:marin-2023-pic", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-image:thumb{{size}}.png" }, "sync_repositum_override": "booktitle", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": 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Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. ", "website": "https://www.cg.tuwien.ac.at/research/area/Geometry", "teaser": { "name": "teaser.png", "path": "research_area:Geometry", "type": "image/png", "size": 168072, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Geometry/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "aichner-2016-sadf", "arikan-2013-osn", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "brunner_lukas_2022_ssb", "celarek-2022-gmcn", "clemenz_2019_rosy_fields", "cornel-2022-wih", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "Doerflinger-2016-CDV", "elagrod_ahmed-baa", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fitzinger_andreas-daa", "forsythe-2016-ccm", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", 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"url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-paper:thumb{{size}}.png" } }, "projects_workgroups": [ { "id": "WorldScale", "workgroup_id": "rend", "drupal_id": 4158, "drupal_path": "/research/projects/WorldScale", "name": "Modeling the World at Scale", "name_de": "Modellierung der Welt nach Maß", "short_title": "WorldScale", "website": null, "status": "active", "short_abstract": "Vision: reconstruct a model of the world that permits online level-of-detail extraction.", "abstract": "

Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/", "__class": "Publication" }, { "id": "marin-2022-sigdt", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/175713", "title": " SIGDT: 2D Curve Reconstruction", "date": "2022-10-05", "abstract": "Determining connectivity between points and reconstructing their shape boundaries are long-standing problems in computer graphics. One possible approach to solve these problems is to use a proximity graph. We propose a new proximity graph computed by intersecting the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG) with the Delaunay triangulation (DT). We prove that the resulting graph, which we name SIGDT, contains the piece-wise linear reconstruction for a set of unstructured points in the plane for a sampling condition superseding current bounds and capturing well practical point sets' properties. As an application, we apply a dual of boundary adjustment steps from the CONNECT2D algorithm to remove the redundant edges. We show that the resulting algorithm SIG-CONNECT2D yields the best reconstruction accuracy compared to state-of-the-art algorithms from a recent comprehensive benchmark, and the method offers the potential for further improvements, e.g., for surface reconstruction.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 7066, "image_height": 3125, "name": "marin-2022-sigdt-Teaser.png", "type": "image/png", "size": 457323, "path": "Publication:marin-2022-sigdt", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Teaser:thumb{{size}}.png" }, "sync_repositum_override": "issn", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": "https://www.cg.tuwien.ac.at/staff/DianaMarin", "firstname": "Diana", "lastname": "Marin", "searchname": "Diana Marin", "is_teacher": false, "name": "Diana Marin", "publications": [ "ehlers-2024-vgs", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "marin-thesis", "ohrhallinger_stefan-2024-inv" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Komon2022", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sig", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sigdt", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2023-pic", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-dsr", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-pcp", "field_id": "lecturer", 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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Involved aspects like handling open, sharp, multiple, and non-manifold outlines, runtime, and provability as well as its extension to 3D for surface reconstruction have led to many different algorithms. The presented algorithms spans the range from improved interpolation of manifold curves over fitting noisy points with better accuracy, requiring fewer points for successful reconstruction to proving the lower limit of required samples with regard to local feature size, or provable statistical accuracy for noise-infected samples. A new sampling condition is introduced that can be expressed as a simple function of the long-standing epsilon-sampling, and permits to reconstruct curves with even fewer samples. As a side product, an algorithm for sampling curves is designed as well. 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online level-of-detail extraction.", "abstract": "

Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-it1/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2022-tut", "type_id": "otherreviewed", "tu_id": null, "repositum_id": null, "title": "Shape Reconstruction: From Theory to Practice", "date": "2022-04-25", "abstract": "Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 476, "image_height": 251, "name": "ohrhallinger_stefan-2022-tut-.JPG", "type": "image/jpeg", "size": 27189, "path": "Publication:ohrhallinger_stefan-2022-tut", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/ohrhallinger_stefan-2022-tut-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/ohrhallinger_stefan-2022-tut-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], 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"slavtchev-dcd", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "starnberger-cli", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "steinheber-2024-oto", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "steinschauer-2020-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "trenovatz-lpd", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "trenovatz-lpd", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "tumpach_barbara-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "vasiljevic-gpc", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "wiesinger_2025-baa", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "wiesinger_klemens-2023-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wildt-tci", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "withalm_brigitte-2022-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wolffhardt-2026-prj", "field_id": "co_supervisor", 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Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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"name_de": "Modellierung der Welt nach Maß", "short_title": "WorldScale", "website": null, "status": "active", "short_abstract": "Vision: reconstruct a model of the world that permits online level-of-detail extraction.", "abstract": "

Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/", "__class": "Publication" }, { "id": "ohrhallinger-2022-e2t", "type_id": "talk", "tu_id": null, "repositum_id": "20.500.12708/153676", "title": "ECCV 2022 Tutorial on Outline and Shape Reconstruction in 2D", "date": "2022", "abstract": "Outline and shape reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. This multitude of reconstruction methods with quite different strengths and focus makes it a difficult task for users to choose a suitable algorithm for their specific problem. In this tutorial, we present proximity graphs, graph-based algorithms, algorithms with sampling guarantees, all in detail. Then, we show algorithms targeted at specific problem classes, such as reconstructing from noise, outliers, or sharp corners. Examples of the evaluation will show how its results can guide users to select an appropriate algorithm for their input data. As a special application, we show reconstruction of lines from sketches that can intersect themselves. Shape characterization of dot patterns will be shown as an additional field closely related to boundary reconstruction.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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However, as soon as we need structures within the point cloud, e.g., for interactions between objects, the construction of state-of-the-art data structures requires O(NlogN) time for N points, which is not feasible in real time for millions of points that are possibly updated in each frame. Therefore, we propose to use a surface representation structure which trades off the (here negligible) disadvantage of single-frame use for both output-dominated and near-linear construction time in practice, exploiting the inherent 2D property of sampled surfaces in 3D. This structure tightly encompasses the assumed surface of unstructured points in a set of bounding depth intervals for each cell of a discrete 2D grid. The sorted depth samples in the structure permit fast surface queries, and on top of that an occlusion graph for the scene comes almost for free. 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for papers.pdf", "ext": "pdf" }, "date_from": "2021-09-06", "date_to": "2021-09-10", "doi": "10.1007/s00371-021-02243-x", "event": "Computer Graphics International 2021", "first_published": "2021-07-28", "issn": "1432-2315", "journal": "The Visual Computer Journal", "lecturer": [ { "id": 923, "drupal_id": 281, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/MohamedRadwan", "firstname": "Mohamed", "lastname": "Radwan", "searchname": "Mohamed Radwan", "is_teacher": false, "name": "Mohamed Radwan", "publications": [ "Kan_Peter-2021-MDPI", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "Doerflinger-2016-CDV", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Radwan_2021_Occ", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "Radwan-2017-Occ", "field_id": "lecturer", "ordernum": 0 } ], "url": 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These models are a demand in many industries, such as entertainment, computer-aided design, urban planning, the fabrication of physical objects, as well as research areas like medical or scientific visualizations and simulations.\n\nWhile rendering - the classical field of computer graphics - has reached very high standards in the recent two decades, the issue of the creation of appropriate digital models is a difficult problem that hardly keeps pace with current demands. Indeed, it is a complex creative process that involves both artistic and technical skills.\n\nTo make this process easier, our goal in this research area is to find new methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "website": "https://www.cg.tuwien.ac.at/research/area/Modeling", "teaser": { "name": "teaser.jpg", "path": "research_area:Modeling", "type": "image/jpeg", "size": 190581, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Modeling/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "AA2020", "Adorjan-2015", "aichner-2016-sadf", "arikan-2013-osn", "birsak-thesis", "Birsak2018-SA", "Brandstaetter2020", "brunner_lukas_2022_ssb", "Caic2021", "clemenz_2019_rosy_fields", "DOG10", "dusberger-2010-msm", "eibens-2021-tsgpmg", "eibensteiner-2021-pmcg", "eibner-2009-gpc", "Erhardt_2025_Scutes", "erler_2024_ppsurf", 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In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

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Classical methods (e.g., Poisson reconstruction) start to degrade in the presence of noisy and partial scans. Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans. However, such data-driven methods struggle to generalize to new shapes with large geometric and topological variations. We present Points2Surf, a novel patch-based learning framework that produces accurate surfaces directly from raw scans without normals.\n\nLearning a prior over a combination of detailed local patches and coarse global information improves generalization performance and reconstruction accuracy.\n\nOur extensive comparison on both synthetic and real data demonstrates a clear advantage of our method over state-of-the-art alternatives on previously unseen classes (on average, Points2Surf brings down reconstruction error by 30% over SPR and by 270%+ over deep learning based SotA methods) at the cost of longer computation times and a slight increase in small-scale topological noise in some cases. \nOur source code, pre-trained model, and dataset are available on: https://github.com/ErlerPhilipp/points2surf\n", "authors_et_al": false, "substitute": null, "main_image": { "description": "We present Points2Surf, a method to reconstruct an accurate implicit surface from a noisy point cloud. Unlike current data-driven surface reconstruction methods like DeepSDF and AtlasNet, it is patch-based, improves detail reconstruction, and unlike Screened Poisson Reconstruction (SPR), a learned prior of low-level patch shapes improves reconstruction accuracy. \nNote the quality of reconstructions, both geometric and topological, against the original surfaces. 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Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. ", "website": "https://www.cg.tuwien.ac.at/research/area/Geometry", "teaser": { "name": "teaser.png", "path": "research_area:Geometry", "type": "image/png", "size": 168072, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Geometry/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "aichner-2016-sadf", "arikan-2013-osn", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "brunner_lukas_2022_ssb", "celarek-2022-gmcn", "clemenz_2019_rosy_fields", "cornel-2022-wih", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "Doerflinger-2016-CDV", "elagrod_ahmed-baa", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fitzinger_andreas-daa", "forsythe-2016-ccm", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "geyer_lukas_2015_prj", "Grossfurtner-2023-paego", "grossmann-2016-baa", "gruber_horst-2019-baa", "Guerrero-2014-TPS", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hladky-2022-QS", "hofer_kerstin-2023-sketch2model", "hoffer-toth-com", "holom_2025-prj", "ILCIK-phdthesis", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "kapferer_2021_advancing", "kazda-tmd", "Kellner-2021-DA", "kerbl-2022-trienc", "koeppel-2016-baa", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "Kugler_2019", "laager_florian-2014-daa", "lang_jakob-2022-baa", "Laschober-2018", "lazarevic-tci", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-rpe", "leimer-2018-sar", "leutschacher_2025_prj", "loeffelmann-1995-para", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-pcp", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "Mayrhauser-2016-Cnc", "mazza-2012-bakk", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "murturi_PGG", "musialski_2016_sosp", "musialski-2012-sur", "musialski-2013-surcgf", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "novak_martin-2018-baa", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-stefan-2012-the", "pakkanen-2026-prj", "pegrisch_2018_uvs", "pichler_2022_fro", "pointner_michael-2017-baa", "pointner_simon-2022-maa", "preiner_2012_AS", "preiner_2017_phd", "Preiner_2019", "preiner2014clop", "proelss-bdp", "prohaska_julia-2023-baa", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis", "rait_alexius-2021-baa", "REICHHOLF-2023-HRO", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "reinwald-2017-baa", "Reisner_2013_RSB", "riegler_2020_framework", "roth_vdi", "rozanics-lsf", "rubik_michael-2022-baa", "rumpelnik_martin_2020_PRM", "schaffarz_2018-pra", "schartmueller_2018_baa", "Scheiblauer-2008-DCW", "SCHEIBLAUER-2009-IDCE", "scheiblauer-2011-cag", "scheiblauer-2011-chnt", "scheiblauer-2012-chnt", "scheiblauer-2013-wscg", "Schmalstieg-1996-LOB", "Schmalstieg-1997-SMO", "scholz_2017_bac", "schrammel_sebastian_baa", "SCHUETZ-2022-PCC", "schuh-2018-mesh-blending", "schulz-faf", "seisenbacher-cyclesafely", "sippl-2014-fss", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steiner_2016_isad", "steinheber-2024-oto", "steinschauer-2020-baa", "steinschorn-2023-p2m", "steinschorn-2025-par", "Tobler-1995-AAC", "Tobler-1995-BGD", "tobler02_amrmg", "Traxler-1996-CTB", "trenovatz-lpd", "tumpach_barbara-pra", "unterguggenberger-2021-msh", "Vucenovic_2022", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "Wimmer-1998-SMO", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2016-HARVEST4D", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa", "Zezulka-2024-FTS" ], "projects": [ "3DSpatialization", "d4388", "d9275", "EVOCATION", "GameTools", "Harvest4D", "SCI_BIM", "ShapeAcquisition", "Superhumans", "TERAPOINTS", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry", "__class": "ResearchArea" }, { "id": "Modeling", "drupal_id": 4130, "name": "Modeling", "short_abstract": "In this area, we focus on researching methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "abstract": "One of the major bottlenecks in the field of computer graphics is the one of content creation, particularly the creation of high-quality 3D geometric models. These models are a demand in many industries, such as entertainment, computer-aided design, urban planning, the fabrication of physical objects, as well as research areas like medical or scientific visualizations and simulations.\n\nWhile rendering - the classical field of computer graphics - has reached very high standards in the recent two decades, the issue of the creation of appropriate digital models is a difficult problem that hardly keeps pace with current demands. Indeed, it is a complex creative process that involves both artistic and technical skills.\n\nTo make this process easier, our goal in this research area is to find new methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "website": "https://www.cg.tuwien.ac.at/research/area/Modeling", "teaser": { "name": "teaser.jpg", "path": "research_area:Modeling", "type": "image/jpeg", "size": 190581, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Modeling/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "AA2020", "Adorjan-2015", "aichner-2016-sadf", "arikan-2013-osn", "birsak-thesis", "Birsak2018-SA", "Brandstaetter2020", "brunner_lukas_2022_ssb", "Caic2021", "clemenz_2019_rosy_fields", "DOG10", "dusberger-2010-msm", "eibens-2021-tsgpmg", "eibensteiner-2021-pmcg", "eibner-2009-gpc", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2016-femfluid-prakt", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fan-2014-scfl", "ferschin-1992-kons", "fiedler-2009-php", "fleiss-2015-da", "GAHR-2013-AGM", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "gervautz-1989-sol1", "gervautz-1993-use", "griessmair-1989-def1", "groeller-1992-frac", "groeller-1995-mod", "Groeller-1995-SDC", "Grossfurtner-2023-paego", "Guerrero-2014-GRF", "Guerrero-2014-TPS", "guerrero-2015-lsp", "HECHER-2017-HDY", "hiess-1992-comb", "hofer_kerstin-2023-sketch2model", "honic-2020-w78", "HU-2017-ETM", "HUMMEL-2001-MSE", "Ilcik_2015_LAY", "ilcik-2010-ps", "ilcik-2013-cipmi", "ilcik-2014-cgbpmi", "ilcik-2016-cmssg", "ilcik-2021-xmas", "kapferer_2021_advancing", "karnik-09-shapegrammar", "Kellner-2021-DA", "kendlbacher-2016", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koenigsberger-2024-msu", "komon-2025-webigeo", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kraehsmaier-dsc", "kreuzer-2016-isf", "kuehtreiber-2010-ikph", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "leimer-2023-ecd", "LEOPOLD-2017-ALG", "LIPP-2007-CGA", "LIPP-2008-IEV", "LIPP-2009-PGL", "LIPP-2009-PGL2", "lipp2011a", "Lupu-2026-Cloth", "macho_jonas-2024-baa", "Maierhofer-thesis", "manpreet_kainth-2012-rus", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_thesis_2019", "mindek-xmas-card-2016", "mujadzic-2023-hfp", "musialski-2009-sbfr", "musialski-2010-pfi", "musialski-2010-tof", "musialski-2012-icb", "musialski-2012-sur", "Musialski-2013-ipmum", "musialski-2013-surcgf", "NIEDERREITER-2010-SIM", "Pahr2020", "Parzer_2013_IIG", "pegrisch_2018_uvs", "Pel-2026", "pogrzebacz-2014-gggm", "polzer-sog", "preiner_2017_phd", "PUEYO-2019-SCL", "RADAX-2010-LOD", "Radwan_2021_Occ", "rasch-2016-imgses", "recheis-2009-arr", "Reichinger-12", "reisinger-2023-iad", "reisner-2010-ind", "reisner-2011-akgeo", "reitsamer-2008-tsud", "Rumpelnik_Martin-2020-NPR", "scharl-master-thesis", "schneider-sturm-2026-mia", "scholz_2017_bac", "SCHWAERZLER-2017-SBGM", "SIETZEN-2017-BBO", "sifuentes-2018-crowd", "sippl-2014-fss", "sperl-2013-BA", "spitaler-2015-pbi", "steiner_2016_isad", "steiner-2014-da", "steinkellner-2022-dll", "steinschorn-2023-p2m", "steinschorn-2025-par", "steinschorn-2025-parameter", "tobler02_amrmg", "tobler02_mbpls", "Traxler-1996-GAI", "VASILJEVS-2018-PMPL", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "weiner_2011", "WIMMER-2015-CGFC", "WIMMER-2016-FUTURIS", "WIMMER-2020-ASG", "wimmer-2023-acd", "Winkler-2017", "WINKLER-2019-PDG", "Wonka-2003-Ins", "xmas-2024", "zeiller-1993-coll", "Zotti-2003-AMP", "zotti-2003-ash", "zotti-2006-pla", "zotti-2007-eg" ], "projects": [ "3DSpatialization", "d4388", "EVOCATION", "GAMEWORLD", "Harvest4D", "PAMINA", "Photo-Guide", "REPLICATE", "SCI_BIM", "Wohnen 4.0" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Modeling", "__class": "ResearchArea" } ], "keywords": [ "surface reconstruction", "implicit surfaces", "point clouds", "patch-based", "local and global", "deep learning", "generalization" ], "weblinks": [ { "href": "https://github.com/ErlerPhilipp/points2surf", "caption": "Github Repo", "description": "Access the source code, pre-trained models and datasets from Github.", "main_file": 0 }, { "href": "https://arxiv.org/abs/2007.10453", "caption": "Arxiv Pre-Print", "description": "Access the paper pre-print on arXiv.", "main_file": 0 }, { "href": "https://link.springer.com/chapter/10.1007%2F978-3-030-58558-7_7", "caption": "Official Publication", "description": "Access the paper Springer Link.", "main_file": 0 } ], "files": [ { "description": "Our normalized ground-truth meshes from the ABC dataset.", "filetitle": "abc_meshes", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-abc_meshes.zip", "type": "application/x-zip-compressed", "size": 726933282, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-abc_meshes.zip", "thumb_image_sizes": [] }, { "description": "Our processed extract from the ABC dataset that can be used to train Points2Surf.", "filetitle": "abc_training", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-abc_training.zip", "type": "application/x-zip-compressed", "size": 1786245314, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-abc_training.zip", "thumb_image_sizes": [] }, { "description": "Our processed extract from the ABC dataset that can be used to replicate the results of Points2Surf.", "filetitle": "abc", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-abc.zip", "type": "application/x-zip-compressed", "size": 138638856, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-abc.zip", "thumb_image_sizes": [] }, { "description": "Our pre-trained models (ablation versions) to reproduce the results.", "filetitle": "ablation_models", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-ablation_models.zip", "type": "application/x-zip-compressed", "size": 242492525, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-ablation_models.zip", "thumb_image_sizes": [] }, { "description": "Our processed Famous dataset that can be used to replicate the results of Points2Surf.", "filetitle": "famous", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-famous.zip", "type": "application/x-zip-compressed", "size": 1525449349, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-famous.zip", "thumb_image_sizes": [] }, { "description": "8 min video for ECCV 2020", "filetitle": "long video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-long video.mp4", "type": "video/mp4", "size": 85439106, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-long video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-long video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-long video:video.mp4" }, { "description": "A new model version with even better results.", "filetitle": "max_model", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-max_model.zip", "type": "application/x-zip-compressed", "size": 20010604, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-max_model.zip", "thumb_image_sizes": [] }, { "description": "Points2Surf paper (Arxiv version)", "filetitle": "points2surf_paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-points2surf_paper.pdf", "type": "application/pdf", "size": 9135835, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-points2surf_paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-points2surf_paper:thumb{{size}}.png" }, { "description": "Our processed real-world dataset that can be used to replicate the results of Points2Surf.", "filetitle": "real_world", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-real_world.zip", "type": "application/x-zip-compressed", "size": 5942947, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-real_world.zip", "thumb_image_sizes": [] }, { "description": "2 min video for ECCV 2020", "filetitle": "short video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-short video.mp4", "type": "video/mp4", "size": 13862480, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-short video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-short video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-short video:video.mp4" }, { "description": "We present Points2Surf, a method to reconstruct an accurate implicit surface from a noisy point cloud. Unlike current data-driven surface reconstruction methods like DeepSDF and AtlasNet, it is patch-based, improves detail reconstruction, and unlike Screened Poisson Reconstruction (SPR), a learned prior of low-level patch shapes improves reconstruction accuracy. \nNote the quality of reconstructions, both geometric and topological, against the original surfaces. The ability of Points2Surf to generalize to new shapes makes it the first learning-based approach with significant generalization ability under both geometric and topological variations.", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 4161, "image_height": 2179, "name": "erler-2020-p2s-teaser.png", "type": "image/png", "size": 4130897, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-teaser:thumb{{size}}.png" }, { "description": "Our processed extract from the Thingi10k dataset that can be used to replicate the results of Points2Surf.", "filetitle": "thingi10k", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-thingi10k.zip", "type": "application/x-zip-compressed", "size": 940826980, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-thingi10k.zip", "thumb_image_sizes": [] }, { "description": "Our pre-trained model (vanilla version) to reproduce the results.", "filetitle": "vanilla_model", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2020-p2s-vanilla_model.zip", "type": "application/x-zip-compressed", "size": 23009153, "path": "Publication:erler-2020-p2s", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/erler-2020-p2s-vanilla_model.zip", "thumb_image_sizes": [] } ], "projects_workgroups": [ { "id": "Superhumans", "workgroup_id": "rend", "drupal_id": 4166, "drupal_path": "/research/projects/Superhumans", "name": "Superhumans - Walking Through Walls", "name_de": null, "short_title": "Superhumans", "website": null, "status": "active", "short_abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "start_date": "2019-09-01", "end_date": "2023-08-31", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:Superhumans", "type": "image/jpeg", "size": 96578, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/Superhumans/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P32418-N31", "comment": "332.780,70 €" } ], "research_areas": [ "Geometry", "VR" ], "publications": [ "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "leimer_2020-cag", "marin-2024-pcp", "parakkat-2024-ballmerge", "rait_alexius-2021-baa", "SCHUETZ-2020-MPC", "schuetz-2020-PPC" ], "url": "https://www.cg.tuwien.ac.at/research/projects/Superhumans", "__class": "Project" }, { "id": "MAKE-IT-FAB", "workgroup_id": "rend", "drupal_id": 4185, "drupal_path": "/research/projects/MAKE-IT-FAB", "name": "MAKE-IT-FAB: Modeling of Shapes for Personal Fabrication", "name_de": null, "short_title": "MAKE-IT-FAB", "website": null, "status": "active", "short_abstract": "The aim of this project is to investigate and to contribute to shape modeling and geometry processing for personal fabrication---a trend that currently receives intensified attention in the science and industry. Our goal is to contribute novel algorithmic solutions for fabrication-aware shape processing and interactive modeling. ", "abstract": "The aim of this project is to investigate and to contribute to shape modeling and geometry processing for personal fabrication---a trend that currently receives intensified attention in the science and industry. \nOur goal is to contribute novel algorithmic solutions for fabrication-aware shape processing and interactive modeling. In particular, we want to research following aspects: (i) We want to address the currently pending problem of high-level shape understanding, (ii) we want to research novel high-level shape modeling methods for interactive creation of plausible and aesthetically appealing shapes, and finally, (iii) we want to address optimization problems related to instant desktop 3D printing, such that modeled objects can be directly fabricated. The outcome of the project will be a set of algorithmic solutions for high-level shape modeling for instant personal fabrication.", "start_date": "2015-04-01", "end_date": "2020-03-31", "leader_id": 844, "logo": { "name": "logo.jpg", "path": "project:MAKE-IT-FAB", "type": "image/jpeg", "size": 80511, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/MAKE-IT-FAB/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P27972-N31", "comment": null } ], "research_areas": [ "Fabrication" ], "publications": [ "aichner-2016-sadf", "birsak-2017-dpe", "birsak-thesis", "Birsak2018-SA", "erler-2020-p2s", "gersthofer-2016-sosob", "hafner-2015-eigf", "hafner-2015-onff", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "musialski_2016_sosp", "musialski-2015-ista", "musialski-2015-pixel", "musialski-2015-souos", "musialski-2015-vrvis", "steiner_2016_isad", "WINKLER-2019-PDG" ], "url": "https://www.cg.tuwien.ac.at/research/projects/MAKE-IT-FAB", "__class": "Project" }, { "id": "ShapeAcquisition", "workgroup_id": "rend", "drupal_id": 4173, "drupal_path": "/research/projects/ShapeAcquisition", "name": "Real-Time Shape Acquisition with Sensor-Specific Precision", "name_de": null, "short_title": "ShapeAcquisition", "website": null, "status": "active", "short_abstract": "Acquiring shapes of physical objects in real time and with guaranteed precision to the noise model of the sensor devices.", "abstract": "

The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.

\r\n", "start_date": "2015-12-01", "end_date": "2020-11-30", "leader_id": 193, "logo": { "name": "logo.jpg", "path": "project:ShapeAcquisition", "type": "image/jpeg", "size": 20004, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P24600-N23", "comment": null } ], "research_areas": [ "Geometry" ], "publications": [ "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "forsythe-2016-ccm", "grossmann-2016-baa", "gruber_horst-2019-baa", "koeppel-2016-baa", "novak_martin-2018-baa", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger-2016-sgp", "pointner_michael-2017-baa", "Radwan_2021_Occ", "Radwan-2017-Occ", "reinwald-2017-baa", "schaffarz_2018-pra", "schartmueller_2018_baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/", "__class": "Publication" }, { "id": "leimer_2020-cag", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/141145", "title": "Pose to Seat: Automated design of body-supporting surfaces", "date": "2020-04", "abstract": "The design of functional seating furniture is a complicated process which often requires extensive manual design effort and empirical evaluation. We propose a computational design framework for pose-driven automated generation of body-supports which are optimized for comfort of sitting. Given a human body in a specified pose as input, our method computes an approximate pressure distribution that also takes frictional forces and body torques into consideration which serves as an objective measure of comfort. Utilizing this information to find out where the body needs to be supported in order to maintain comfort of sitting, our algorithm can create a supporting mesh suited for a person in that specific pose. This is done in an automated fitting process, using a template model capable of supporting a large variety of sitting poses. The results can be used directly or can be considered as a starting point for further interactive design.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 627, "image_height": 636, "name": "leimer_2020-cag-image.png", "type": "image/png", "size": 270901, "path": "Publication:leimer_2020-cag", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/leimer_2020-cag-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/leimer_2020-cag-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1019, "drupal_id": 609, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/KurtLeimer", "firstname": "Kurt", "lastname": "Leimer", "searchname": "Kurt Leimer", "is_teacher": false, 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"event": "Conference", "journal": "Computer Aided Geometric Design", "open_access": "yes", "pages_from": "1", "pages_to": "1", "volume": "79", "research_areas": [ { "id": "Fabrication", "drupal_id": 4132, "name": "Fabrication", "short_abstract": "The advent of consumer-grade 3D printing has recently sparked interest in fabrication-aware shape design and optimization. We use techniques from computer graphics and engineering to develop new computational tools to facilitate the design process of physical artifacts.", "abstract": "The task of finding the perfect shape of a real-world object that fulfills a given objective optimally appears in many disciplines, like interior design, building planning, or construction of machine parts. These tasks can be subject to considerations of functionality, cost-effciency, and aesthetics, as well as constraints relating to size, producibility, safety margins, etc. The great complexity and the presence of conflicting and hard-to-formalize goals have hitherto precluded automation of many design tasks and leave them in the hands of human designers and engineers.\n\nThere is, however, great potential to aid these processes computationally and to unlock difficult design tasks for a larger audience. This will not only provide faster work flows and enable the exploration of new design spaces, but it constitutes a necessary step towards the wide-spread application of personalized fabrication techniques like additive manufacturing. Low-cost 3d printing has been widely described as a disruptive technology and the herald of a new industrial revolution. These expectations have since been tempered by a reality which shows that availability alone is inconsequential unless accompanied by an innovation of supporting technologies.\n\nThe big challenge in making new fabrication techniques more useful is the development of a computational design methodology that relieves its users of low-level tasks like explicit 3d modeling and provides tools to create shapes from functional, aesthetic, and other high-level specifications. The overarching goal of this research area is to find new mathematical models and computational solutions fordesign tasks that are at this time tedious or impossible at all.", "website": "https://www.cg.tuwien.ac.at/research/area/Fabrication", "teaser": { "name": "teaser.png", "path": "research_area:Fabrication", "type": "image/png", "size": 420422, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Fabrication/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "Birsak2018-SA", "gersthofer-2016-sosob", "hafner-2015-eigf", "hafner-2015-onff", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "leimer_2020-cag", "leimer-2018-sar", "musialski_2016_sosp", "musialski-2015-souos", "pahr-2021-vologram", "pahr-2024-squishicalization", "Reichinger-2016-spaghetti", "schindler_2020vis", "schultes_lampi", "Staudacher_2022", "walchhofer-tso", "WINKLER-2019-PDG" ], "projects": [ "EVOCATION", "MAKE-IT-FAB" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Fabrication", "__class": "ResearchArea" }, { "id": "Geometry", "drupal_id": 4129, "name": "Geometry Processing", "short_abstract": "Uses concepts from applied mathematics and computer science to design efficient algorithms for the reconstruction, analysis, manipulation, simulation and transmission of complex 3D models. Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. ", "website": "https://www.cg.tuwien.ac.at/research/area/Geometry", "teaser": { "name": "teaser.png", "path": "research_area:Geometry", "type": "image/png", "size": 168072, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Geometry/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "aichner-2016-sadf", "arikan-2013-osn", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "brunner_lukas_2022_ssb", "celarek-2022-gmcn", "clemenz_2019_rosy_fields", "cornel-2022-wih", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "Doerflinger-2016-CDV", "elagrod_ahmed-baa", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fitzinger_andreas-daa", "forsythe-2016-ccm", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "geyer_lukas_2015_prj", "Grossfurtner-2023-paego", "grossmann-2016-baa", "gruber_horst-2019-baa", "Guerrero-2014-TPS", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hladky-2022-QS", "hofer_kerstin-2023-sketch2model", "hoffer-toth-com", "holom_2025-prj", "ILCIK-phdthesis", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "kapferer_2021_advancing", "kazda-tmd", "Kellner-2021-DA", "kerbl-2022-trienc", "koeppel-2016-baa", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "Kugler_2019", "laager_florian-2014-daa", "lang_jakob-2022-baa", "Laschober-2018", "lazarevic-tci", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-rpe", "leimer-2018-sar", "leutschacher_2025_prj", "loeffelmann-1995-para", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-pcp", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "Mayrhauser-2016-Cnc", "mazza-2012-bakk", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "murturi_PGG", "musialski_2016_sosp", "musialski-2012-sur", "musialski-2013-surcgf", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "novak_martin-2018-baa", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-stefan-2012-the", "pakkanen-2026-prj", "pegrisch_2018_uvs", "pichler_2022_fro", "pointner_michael-2017-baa", "pointner_simon-2022-maa", "preiner_2012_AS", "preiner_2017_phd", "Preiner_2019", "preiner2014clop", "proelss-bdp", "prohaska_julia-2023-baa", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis", "rait_alexius-2021-baa", "REICHHOLF-2023-HRO", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "reinwald-2017-baa", "Reisner_2013_RSB", "riegler_2020_framework", "roth_vdi", "rozanics-lsf", "rubik_michael-2022-baa", "rumpelnik_martin_2020_PRM", "schaffarz_2018-pra", "schartmueller_2018_baa", "Scheiblauer-2008-DCW", "SCHEIBLAUER-2009-IDCE", "scheiblauer-2011-cag", "scheiblauer-2011-chnt", "scheiblauer-2012-chnt", "scheiblauer-2013-wscg", "Schmalstieg-1996-LOB", "Schmalstieg-1997-SMO", "scholz_2017_bac", "schrammel_sebastian_baa", "SCHUETZ-2022-PCC", "schuh-2018-mesh-blending", "schulz-faf", "seisenbacher-cyclesafely", "sippl-2014-fss", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steiner_2016_isad", "steinheber-2024-oto", "steinschauer-2020-baa", "steinschorn-2023-p2m", "steinschorn-2025-par", "Tobler-1995-AAC", "Tobler-1995-BGD", "tobler02_amrmg", "Traxler-1996-CTB", "trenovatz-lpd", "tumpach_barbara-pra", "unterguggenberger-2021-msh", "Vucenovic_2022", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "Wimmer-1998-SMO", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2016-HARVEST4D", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa", "Zezulka-2024-FTS" ], "projects": [ "3DSpatialization", "d4388", "d9275", "EVOCATION", "GameTools", "Harvest4D", "SCI_BIM", "ShapeAcquisition", "Superhumans", "TERAPOINTS", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry", "__class": "ResearchArea" }, { "id": "Modeling", "drupal_id": 4130, "name": "Modeling", "short_abstract": "In this area, we focus on researching methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "abstract": "One of the major bottlenecks in the field of computer graphics is the one of content creation, particularly the creation of high-quality 3D geometric models. These models are a demand in many industries, such as entertainment, computer-aided design, urban planning, the fabrication of physical objects, as well as research areas like medical or scientific visualizations and simulations.\n\nWhile rendering - the classical field of computer graphics - has reached very high standards in the recent two decades, the issue of the creation of appropriate digital models is a difficult problem that hardly keeps pace with current demands. Indeed, it is a complex creative process that involves both artistic and technical skills.\n\nTo make this process easier, our goal in this research area is to find new methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "website": "https://www.cg.tuwien.ac.at/research/area/Modeling", "teaser": { "name": "teaser.jpg", "path": "research_area:Modeling", "type": "image/jpeg", "size": 190581, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Modeling/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "AA2020", "Adorjan-2015", "aichner-2016-sadf", "arikan-2013-osn", "birsak-thesis", "Birsak2018-SA", "Brandstaetter2020", "brunner_lukas_2022_ssb", "Caic2021", "clemenz_2019_rosy_fields", "DOG10", "dusberger-2010-msm", "eibens-2021-tsgpmg", "eibensteiner-2021-pmcg", "eibner-2009-gpc", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2016-femfluid-prakt", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fan-2014-scfl", "ferschin-1992-kons", "fiedler-2009-php", "fleiss-2015-da", "GAHR-2013-AGM", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "gervautz-1989-sol1", "gervautz-1993-use", "griessmair-1989-def1", "groeller-1992-frac", "groeller-1995-mod", "Groeller-1995-SDC", "Grossfurtner-2023-paego", "Guerrero-2014-GRF", "Guerrero-2014-TPS", "guerrero-2015-lsp", "HECHER-2017-HDY", "hiess-1992-comb", "hofer_kerstin-2023-sketch2model", "honic-2020-w78", "HU-2017-ETM", "HUMMEL-2001-MSE", "Ilcik_2015_LAY", "ilcik-2010-ps", "ilcik-2013-cipmi", "ilcik-2014-cgbpmi", "ilcik-2016-cmssg", "ilcik-2021-xmas", "kapferer_2021_advancing", "karnik-09-shapegrammar", "Kellner-2021-DA", "kendlbacher-2016", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koenigsberger-2024-msu", "komon-2025-webigeo", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kraehsmaier-dsc", "kreuzer-2016-isf", "kuehtreiber-2010-ikph", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "leimer-2023-ecd", "LEOPOLD-2017-ALG", "LIPP-2007-CGA", "LIPP-2008-IEV", "LIPP-2009-PGL", "LIPP-2009-PGL2", "lipp2011a", "Lupu-2026-Cloth", "macho_jonas-2024-baa", "Maierhofer-thesis", "manpreet_kainth-2012-rus", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_thesis_2019", "mindek-xmas-card-2016", "mujadzic-2023-hfp", "musialski-2009-sbfr", "musialski-2010-pfi", "musialski-2010-tof", "musialski-2012-icb", "musialski-2012-sur", "Musialski-2013-ipmum", "musialski-2013-surcgf", "NIEDERREITER-2010-SIM", "Pahr2020", "Parzer_2013_IIG", "pegrisch_2018_uvs", "Pel-2026", "pogrzebacz-2014-gggm", "polzer-sog", "preiner_2017_phd", "PUEYO-2019-SCL", "RADAX-2010-LOD", "Radwan_2021_Occ", "rasch-2016-imgses", "recheis-2009-arr", "Reichinger-12", "reisinger-2023-iad", "reisner-2010-ind", "reisner-2011-akgeo", "reitsamer-2008-tsud", "Rumpelnik_Martin-2020-NPR", "scharl-master-thesis", "schneider-sturm-2026-mia", "scholz_2017_bac", "SCHWAERZLER-2017-SBGM", "SIETZEN-2017-BBO", "sifuentes-2018-crowd", "sippl-2014-fss", "sperl-2013-BA", "spitaler-2015-pbi", "steiner_2016_isad", "steiner-2014-da", "steinkellner-2022-dll", "steinschorn-2023-p2m", "steinschorn-2025-par", "steinschorn-2025-parameter", "tobler02_amrmg", "tobler02_mbpls", "Traxler-1996-GAI", "VASILJEVS-2018-PMPL", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "weiner_2011", "WIMMER-2015-CGFC", "WIMMER-2016-FUTURIS", "WIMMER-2020-ASG", "wimmer-2023-acd", "Winkler-2017", "WINKLER-2019-PDG", "Wonka-2003-Ins", "xmas-2024", "zeiller-1993-coll", "Zotti-2003-AMP", "zotti-2003-ash", "zotti-2006-pla", "zotti-2007-eg" ], "projects": [ "3DSpatialization", "d4388", "EVOCATION", "GAMEWORLD", "Harvest4D", "PAMINA", "Photo-Guide", "REPLICATE", "SCI_BIM", "Wohnen 4.0" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Modeling", "__class": "ResearchArea" } ], "keywords": [ "pose estimation", "furniture", "computational design" ], "weblinks": [ { "href": "https://arxiv.org/pdf/2003.10435.pdf", "caption": "paper", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 627, "image_height": 636, "name": "leimer_2020-cag-image.png", "type": "image/png", "size": 270901, "path": "Publication:leimer_2020-cag", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/leimer_2020-cag-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/leimer_2020-cag-image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "name": "leimer_2020-cag-Paper.pdf", "type": "application/pdf", "size": 3539714, "path": "Publication:leimer_2020-cag", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/leimer_2020-cag-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/leimer_2020-cag-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "Superhumans", "workgroup_id": "rend", "drupal_id": 4166, "drupal_path": "/research/projects/Superhumans", "name": "Superhumans - Walking Through Walls", "name_de": null, "short_title": "Superhumans", "website": null, "status": "active", "short_abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "start_date": "2019-09-01", "end_date": "2023-08-31", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:Superhumans", "type": "image/jpeg", "size": 96578, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/Superhumans/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P32418-N31", "comment": "332.780,70 €" } ], "research_areas": [ "Geometry", "VR" ], "publications": [ "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "leimer_2020-cag", "marin-2024-pcp", "parakkat-2024-ballmerge", "rait_alexius-2021-baa", "SCHUETZ-2020-MPC", "schuetz-2020-PPC" ], "url": "https://www.cg.tuwien.ac.at/research/projects/Superhumans", "__class": "Project" }, { "id": "MAKE-IT-FAB", "workgroup_id": "rend", "drupal_id": 4185, "drupal_path": "/research/projects/MAKE-IT-FAB", "name": "MAKE-IT-FAB: Modeling of Shapes for Personal Fabrication", "name_de": null, "short_title": "MAKE-IT-FAB", "website": null, "status": "active", "short_abstract": "The aim of this project is to investigate and to contribute to shape modeling and geometry processing for personal fabrication---a trend that currently receives intensified attention in the science and industry. Our goal is to contribute novel algorithmic solutions for fabrication-aware shape processing and interactive modeling. ", "abstract": "The aim of this project is to investigate and to contribute to shape modeling and geometry processing for personal fabrication---a trend that currently receives intensified attention in the science and industry. \nOur goal is to contribute novel algorithmic solutions for fabrication-aware shape processing and interactive modeling. In particular, we want to research following aspects: (i) We want to address the currently pending problem of high-level shape understanding, (ii) we want to research novel high-level shape modeling methods for interactive creation of plausible and aesthetically appealing shapes, and finally, (iii) we want to address optimization problems related to instant desktop 3D printing, such that modeled objects can be directly fabricated. The outcome of the project will be a set of algorithmic solutions for high-level shape modeling for instant personal fabrication.", "start_date": "2015-04-01", "end_date": "2020-03-31", "leader_id": 844, "logo": { "name": "logo.jpg", "path": "project:MAKE-IT-FAB", "type": "image/jpeg", "size": 80511, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/MAKE-IT-FAB/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P27972-N31", "comment": null } ], "research_areas": [ "Fabrication" ], "publications": [ "aichner-2016-sadf", "birsak-2017-dpe", "birsak-thesis", "Birsak2018-SA", "erler-2020-p2s", "gersthofer-2016-sosob", "hafner-2015-eigf", "hafner-2015-onff", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "musialski_2016_sosp", "musialski-2015-ista", "musialski-2015-pixel", "musialski-2015-souos", "musialski-2015-vrvis", "steiner_2016_isad", "WINKLER-2019-PDG" ], "url": "https://www.cg.tuwien.ac.at/research/projects/MAKE-IT-FAB", "__class": "Project" }, { "id": "geo-materials", "workgroup_id": null, "drupal_id": 4187, "drupal_path": "/research/projects/geo-materials", "name": "Computational Design of Geometric Materials", "name_de": null, "short_title": "geo-materials", "website": null, "status": "active", "short_abstract": "In this project we want to research novel materials whose mechanical behavior is described by the complexity of their geometry. Such “geometric materials” are cellular structures whose properties depend on the shape and the connectivity of their cells, while the actual physical substance they are built of is constant across the entire object. ", "abstract": "

In this project we want to research novel materials whose mechanical behavior is described by the complexity of their geometry. Such “geometric materials” are cellular structures whose properties depend on the shape and the connectivity of their cells, while the actual physical substance they are built of is constant across the entire object. Our goal is to develop the first computational model for the analysis and simulation of complex geometric materials, as well as a so-called goal-based computational design framework for their synthesis. Goal-based means that the desired behavior can be specified a-priori by the designer, and an appropriate geometric structure that best approximates the given goals is computed automatically. Our main research problem is how to map mechanical properties to geometric connections of cellular structures.

\r\n", "start_date": "2016-03-01", "end_date": "2021-08-31", "leader_id": 844, "logo": { "name": "logo.png", "path": "project:geo-materials", "type": "image/png", "size": 298337, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/geo-materials/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": null, "comment": null } ], "research_areas": [], "publications": [ "leimer_2020-cag", "leimer-2018-sar" ], "url": "https://www.cg.tuwien.ac.at/research/projects/geo-materials", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/leimer_2020-cag/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2018-cgf", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "FitConnect: Connecting Noisy 2D Samples by Fitted Neighborhoods", "date": "2019-02", "abstract": "We propose a parameter-free method to recover manifold connectivity in unstructured 2D point clouds with high noise in terms of the local feature size. This enables us to capture the features which emerge out of the noise. To achieve this, we extend the reconstruction algorithm HNN-Crust, which connects samples to two (noise-free) neighbors and has been proven to output a manifold for a relaxed sampling condition. Applying this condition to noisy samples by projecting their k-nearest neighborhoods onto local circular fits leads to multiple candidate neighbor pairs and thus makes connecting them consistently an NP-hard problem. To solve this efficiently, we design an algorithm that searches that solution space iteratively on different scales of k. It achieves linear time complexity in terms of point count plus quadratic time in the size of noise clusters. Our algorithm FitConnect extends HNN-Crust seamlessly to connect both samples with and without noise, performs as local as the recovered features and can output multiple open or closed piece-wise curves. Incidentally, our method simplifies the output geometry by eliminating all but a representative point from noisy clusters. Since local neighborhood fits overlap consistently, the resulting connectivity represents an ordering of the samples along a manifold. This permits us to simply blend the local fits for denoising with the locally estimated noise extent. Aside from applications like reconstructing silhouettes of noisy sensed data, this lays important groundwork to improve surface reconstruction in 3D. Our open-source algorithm is available online.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Manifold curve fitted samples with highly varying noise", "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 704, "image_height": 704, "name": "ohrhallinger_stefan-2018-cgf-image.png", "type": "image/png", "size": 12741, "path": "Publication:ohrhallinger_stefan-2018-cgf", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2018-cgf/ohrhallinger_stefan-2018-cgf-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2018-cgf/ohrhallinger_stefan-2018-cgf-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": 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The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.

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"url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2018-cgf/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2019-xms", "type_id": "xmascard", "tu_id": null, "repositum_id": null, "title": "X-Mas Card 2019", "date": "2019", "abstract": "See right for correct solution of our connect-the-dots game :-)\nOf course, we not only reconstruct members of our institute but also\nhighly noisy point clouds, additionally denoise the reconstruction,\nand specify the minimum number of samples required for that.\nEduard Gröller\nSee here for the mystery present in the crib: youtu.be/-oVwXaaJNtY\n\nDie Auflösung unseres Punkte-verbinden-Spiels ist hier rechts :-)\nWir rekonstruieren nicht nur Institutsmitglieder, sondern auch\nstark verrauschte Punktewolken, entfernen das Rauschen\nund berechnen die Mindestanzahl der benötigten Messpunkte.\nHier ist das Geschenk in der Krippe zu sehen: youtu.be/-oVwXaaJNtY", "authors_et_al": false, "substitute": null, 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{ "id": "xmas", "workgroup_id": "abteilung", "drupal_id": 4153, "drupal_path": "/research/projects/xmas", "name": "X-Mas Cards", "name_de": null, "short_title": "xmas", "website": null, "status": "active", "short_abstract": "Every year a christmas card showing aspects of our research projects is produced and sent out.", "abstract": "

Every year a christmas card showing aspects of our research projects is produced and sent out.

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The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.

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"publ_id": "Zezulka_Matthias-2023-MatLabOptInterface", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Zezulka-2024-FTS", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "Zezulka-2025-NRL", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "Zezulka-2026-FGTS", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Zezulka-2026-GridThermalSim", "field_id": "supervisor", "ordernum": 2 }, { "publ_id": "zhang-thesis", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "ZL_2011_IVIG", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "zsolnai-feher-thesis-2019", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=193", "__class": "Author" } ], "booktitle": "Proceedings of Graphics Interface 2017", "date_from": "2017-05-17", "date_to": "2017-05-19", "doi": "10.20380/GI2017.11", "event": "Graphics Interface 2017", "lecturer": [ { "id": 923, "drupal_id": 281, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/MohamedRadwan", "firstname": "Mohamed", "lastname": "Radwan", "searchname": "Mohamed Radwan", "is_teacher": false, "name": "Mohamed Radwan", "publications": [ "Kan_Peter-2021-MDPI", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "Doerflinger-2016-CDV", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Radwan_2021_Occ", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "Radwan-2017-Occ", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=923", "__class": "Author" } ], "location": "Edmonton, Alberta, CA", "pages_from": "82", "pages_to": "89", "publisher": "Canadian Human-Computer Communications Society / Soci{\\'e}t{\\'e} canadienne du dialogue humain-machine", "research_areas": [ { "id": "Geometry", "drupal_id": 4129, "name": "Geometry Processing", "short_abstract": "Uses concepts from applied mathematics and computer science to design efficient algorithms for the reconstruction, analysis, manipulation, simulation and transmission of complex 3D models. Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. ", "website": "https://www.cg.tuwien.ac.at/research/area/Geometry", "teaser": { "name": "teaser.png", "path": "research_area:Geometry", "type": "image/png", "size": 168072, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Geometry/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "aichner-2016-sadf", "arikan-2013-osn", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "brunner_lukas_2022_ssb", "celarek-2022-gmcn", "clemenz_2019_rosy_fields", "cornel-2022-wih", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "Doerflinger-2016-CDV", "elagrod_ahmed-baa", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fitzinger_andreas-daa", "forsythe-2016-ccm", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "geyer_lukas_2015_prj", "Grossfurtner-2023-paego", "grossmann-2016-baa", "gruber_horst-2019-baa", "Guerrero-2014-TPS", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hladky-2022-QS", "hofer_kerstin-2023-sketch2model", "hoffer-toth-com", "holom_2025-prj", "ILCIK-phdthesis", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "kapferer_2021_advancing", "kazda-tmd", "Kellner-2021-DA", "kerbl-2022-trienc", "koeppel-2016-baa", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "Kugler_2019", "laager_florian-2014-daa", "lang_jakob-2022-baa", "Laschober-2018", "lazarevic-tci", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-rpe", "leimer-2018-sar", "leutschacher_2025_prj", "loeffelmann-1995-para", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-pcp", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "Mayrhauser-2016-Cnc", "mazza-2012-bakk", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "murturi_PGG", "musialski_2016_sosp", "musialski-2012-sur", "musialski-2013-surcgf", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "novak_martin-2018-baa", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-stefan-2012-the", "pakkanen-2026-prj", "pegrisch_2018_uvs", "pichler_2022_fro", "pointner_michael-2017-baa", "pointner_simon-2022-maa", "preiner_2012_AS", "preiner_2017_phd", "Preiner_2019", "preiner2014clop", "proelss-bdp", "prohaska_julia-2023-baa", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis", "rait_alexius-2021-baa", "REICHHOLF-2023-HRO", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "reinwald-2017-baa", "Reisner_2013_RSB", "riegler_2020_framework", "roth_vdi", "rozanics-lsf", "rubik_michael-2022-baa", "rumpelnik_martin_2020_PRM", "schaffarz_2018-pra", "schartmueller_2018_baa", "Scheiblauer-2008-DCW", "SCHEIBLAUER-2009-IDCE", "scheiblauer-2011-cag", "scheiblauer-2011-chnt", "scheiblauer-2012-chnt", "scheiblauer-2013-wscg", "Schmalstieg-1996-LOB", "Schmalstieg-1997-SMO", "scholz_2017_bac", "schrammel_sebastian_baa", "SCHUETZ-2022-PCC", "schuh-2018-mesh-blending", "schulz-faf", "seisenbacher-cyclesafely", "sippl-2014-fss", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steiner_2016_isad", "steinheber-2024-oto", "steinschauer-2020-baa", "steinschorn-2023-p2m", "steinschorn-2025-par", "Tobler-1995-AAC", "Tobler-1995-BGD", "tobler02_amrmg", "Traxler-1996-CTB", "trenovatz-lpd", "tumpach_barbara-pra", "unterguggenberger-2021-msh", "Vucenovic_2022", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "Wimmer-1998-SMO", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2016-HARVEST4D", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa", "Zezulka-2024-FTS" ], "projects": [ "3DSpatialization", "d4388", "d9275", "EVOCATION", "GameTools", "Harvest4D", "SCI_BIM", "ShapeAcquisition", "Superhumans", "TERAPOINTS", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry", "__class": "ResearchArea" }, { "id": "IllVis", "drupal_id": 4131, "name": "Illustrative Visualization", "short_abstract": "In this research area, we develop rendering methods that are inspired by scientific illustrations, in order to make complex biological information more intuitive to understand and more pleasant to read.", "abstract": "Scientific illustration is concerned with researching ways in which complex biological and medical data can be abstracted in order to convey a desired information that would otherwise be impossible or hard to retrieve from the original data. Illustrators apply such techniques, for instance, to present non-important parts of an organ in a simplified way, while using detailed representations to put the focus of attention on a region where a tumor might be situated.\n\nTo identify such meaningful visual abstractions for visualization, their effectiveness for human perception and cognition has to be assessed, and they have to be formalized in order to make them applicable in rendering algorithms. In this project, we try to advance the understanding of procedural visual abstraction of complex physiological processes that describe the complex machinery of biological life.", "website": "https://www.cg.tuwien.ac.at/research/area/IllVis", "teaser": { "name": "teaser.jpg", "path": "research_area:IllVis", "type": "image/jpeg", "size": 34861, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/IllVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "antes-2026-maa", "Antonini2020", "Balabanian-2010-IIV", "Balduz_01", "Bayat_2019", "Bechtold2020", "Birkeland-2012-IMC", "brandstaetter_01", "bruckner-2005-ICV", "bruckner-2006-ICE", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2010-HVC", "bruckner-2010-IFC", "cellVIEW_2015", "Chen-Information-2016", "Deutsch-2018", "Dhanoa_2021", "Dhanoa2024", "dietrich-2016-viseq2", "donabauer_2019_1", "eg-tut2005-iv", "eschner-2023-ims", "Essler2022", "gangl-2018-gpsart", "Gehrer_Daniel_CUI", "Gehrer-2017-molmach", "gerl-2006-vhi", "Gogel2020", "Groeller_2011_IPV", "Groeller_2016_I3", "Groeller_2016_I6", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_V1_2020", "groeller-2006-ivt", "groeller-2007-fci", "groeller-2008-irs", "Groeller-2010-IFI", "gruber-2025-vps", "Halladjian_2020", "haushofer-2018-skinning", "Heim_2020", "horvath-2018-ism", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Knapp_2017_CVR", "Koeppel2020", "kolesar-ivan-2014-polymers", "Konev-FCV2018", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "kroesl_x_card_2017", "Lackner_2022", "Langer_Edith_IR1", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "Leutschacher_2022", "ludwig-2012-MT", "Manczarski-2016", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "Melo_2018_11", "Meusburger_2020", "Miao_2019", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao2018Dimsum", "Michl_2017_CSV", "mindek-2015-mc", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-xmas-card-2016", "Moellinger_Christian_IDE2", "Niedermayer_2018", "Ortner_PhD", "Ortner2020", "pahr-2021-vologram", "Panayotov", "patel_daniel_2007_IRSD", "Pauschenwein_2022", "Peter_2012_AIV", "plank-2017-sldg", "prost-2016-molecule", "purgathofer-2008-cgi", "Radwan-2017-Occ", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-VF", "Reichinger_2016", "Reichinger_Fuhrmann_2016", "Reisacher_Matthias_CPW", "Reisacher2016", "Riegelnegg_2019", "rippl-2023-atg", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "schindler_2020vis", "schmidlehner2021", "scholz_2021", "Schrempf_2020", "schultes_lampi", "seyfert-2017", "sikachev-2010-ill_vis_vol_ren", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "Spechtenhauser_Florian_2016", "stappen_2017_animatedcomic", "Stoll2020", "sturl_2016_dynlabels", "Swoboda2020", "szabo_patrik-2022-tour", "takahashi-2019-acdt", "Thomas-2018", "TR-186-2-07-08", "Troidl_2021", "Troidl_2022", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "Unger2020", "Vasileva_2022", "Viola_Ivan_2013_SVA", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "viola-evr", "Viola-Pondering-2017", "Viola-vistutillustrativevis", "Waldin_Nicholas_2016_Colormaps", "waldin-2019-ccm", "Waldner_2017_11", "Wihann_2017", "Woschizka_2021", "wu-2019-bmc", "wu-2019-report", "Zaufel_2021", "zechmeister2020", "Zeng_2021", "Zusag-2017-Bach" ], "projects": [ "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=IllVis", "__class": "ResearchArea" } ], "keywords": [], "weblinks": [], "files": [ { "description": "Occlusion-aware generation of a cutaway illustration.", "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1964, "image_height": 2040, "name": "Radwan-2017-Occ-image.jpg", "type": "image/jpeg", "size": 735971, "path": "Publication:Radwan-2017-Occ", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Radwan-2017-Occ/Radwan-2017-Occ-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Radwan-2017-Occ/Radwan-2017-Occ-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Radwan-2017-Occ-paper.pdf", "type": "application/pdf", "size": 5813944, "path": "Publication:Radwan-2017-Occ", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Radwan-2017-Occ/Radwan-2017-Occ-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/Radwan-2017-Occ/Radwan-2017-Occ-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "ShapeAcquisition", "workgroup_id": "rend", "drupal_id": 4173, "drupal_path": "/research/projects/ShapeAcquisition", "name": "Real-Time Shape Acquisition with Sensor-Specific Precision", "name_de": null, "short_title": "ShapeAcquisition", "website": null, "status": "active", "short_abstract": "Acquiring shapes of physical objects in real time and with guaranteed precision to the noise model of the sensor devices.", "abstract": "

The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.

\r\n", "start_date": "2015-12-01", "end_date": "2020-11-30", "leader_id": 193, "logo": { "name": "logo.jpg", "path": "project:ShapeAcquisition", "type": "image/jpeg", "size": 20004, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P24600-N23", "comment": null } ], "research_areas": [ "Geometry" ], "publications": [ "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "forsythe-2016-ccm", "grossmann-2016-baa", "gruber_horst-2019-baa", "koeppel-2016-baa", "novak_martin-2018-baa", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger-2016-sgp", "pointner_michael-2017-baa", "Radwan_2021_Occ", "Radwan-2017-Occ", "reinwald-2017-baa", "schaffarz_2018-pra", "schartmueller_2018_baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Radwan-2017-Occ/", "__class": "Publication" }, { "id": "ohrhallinger-2016-sgp", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Curve Reconstruction with Many Fewer Samples", "date": "2016", "abstract": "We consider the problem of sampling points from a collection of smooth curves in the plane, such that the Crust family of proximity-based reconstruction algorithms can rebuild the curves. Reconstruction requires a dense sampling of local features, i.e., parts of the curve that are close in Euclidean distance but far apart geodesically.\nWe show that epsilon<0.47-sampling is sufficient for our proposed HNN-CRUST variant, improving upon the \nstate-of-the-art requirement of epsilon<1/3-sampling.\nThus we may reconstruct curves with many fewer samples.\nWe also present a new sampling scheme that reduces the required density even further than epsilon<0.47-sampling.\nWe achieve this by better controlling the spacing between geodesically consecutive points.\nOur novel sampling condition is based on the reach, the minimum local feature size along intervals between samples.\nThis is mathematically closer to the reconstruction density requirements, particularly near sharp-angled features.\nWe prove lower and upper bounds on reach rho-sampling density in terms of lfs epsilon-sampling and demonstrate that we typically reduce the required number of samples for reconstruction by more than half.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2000, "image_height": 1200, "name": "ohrhallinger-2016-sgp-image.png", "type": "image/png", "size": 54228, "path": "Publication:ohrhallinger-2016-sgp", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": 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4207, "drupal_path": "/research/projects/Harvest4D", "name": "Harvest4D - Harvesting Dynamic 3D Worlds from Commodity Sensor Clouds", "name_de": null, "short_title": "Harvest4D", "website": null, "status": "active", "short_abstract": "Harvest4D investigates the whole acquisition, modeling and rendering pipeline for incidental data capture.", "abstract": "

The current acquisition pipeline for visual models of 3D worlds is based on a paradigm of planning a goal-oriented acquisition - sampling on site - processing. The digital model of an artifact (an object, a building,up to an entire city) is produced by planning a specific scanning campaign, carefully selecting the (often costly) acquisition devices, performing the on-site acquisition at the required resolution and then post-processing the acquired data to produce a beautified triangulated and textured model. However, in the future we will be faced with the ubiquitous availability of sensing devices that deliver different data streams that need to be processed and displayed in a new way, for example smartphones, commodity stereo cameras, cheap aerial data acquisition devices, etc. We therefore propose a radical paradigm change in acquisition and processing technology: instead of a goal-driven acquisition that determines the devices and sensors, we let the sensors and resulting available data determine the acquisition process. Data acquisition might become incidental to other tasks that evices/people to which sensors are attached carry out. A variety of challenging problems need to be solved to exploit this huge amount of data, including: dealing with continuous streams of time-dependent data, finding means of integrating data from different sensors and modalities, detecting changes in data sets to create 4D models, harvesting data to go beyond simple 3D geometry, and researching new paradigms for interactive inspection capabilities with 4D data sets. In this project, we envision solutions to these challenges, paving the way for affordable and innovative uses of information technology in an evolving world sampled by ubiquitous visual sensors. Our approach is high-risk and an enabling factor for future visual applications. The focus is clearly on basic research questions to lay the foundation for the new paradigm of incidental 4D data capture.

\r\n", "start_date": "2013-06-01", "end_date": "2016-07-31", "leader_id": 193, "logo": { "name": "logo.png", "path": "project:Harvest4D", "type": "image/png", "size": 494905, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/Harvest4D/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "eu7", "contract_number": "323567", "comment": null } ], "research_areas": [ "Geometry", "Modeling", "Rendering" ], "publications": [ "arikan-2014-pcvis", "arikan-2015-dmrt", "laager_florian-2014-daa", "Mayrhauser-2016-Cnc", "MAYRHAUSER-2016-SCA", "ohrhallinger-2016-sgp", "preiner2014clop", "Radwan-2014-CDR", "scheiblauer-thesis", "SCHUETZ-2014-RRLP", "SCHUETZ-2015-HQP", "SCHUETZ-2016-POT", "WIMMER-2014-DWNT", "WIMMER-2016-FUTURIS", "WIMMER-2016-HARVEST4D" ], "url": "https://www.cg.tuwien.ac.at/research/projects/Harvest4D", "__class": "Project" }, { "id": "ShapeAcquisition", "workgroup_id": "rend", "drupal_id": 4173, "drupal_path": "/research/projects/ShapeAcquisition", "name": "Real-Time Shape Acquisition with Sensor-Specific Precision", "name_de": null, "short_title": "ShapeAcquisition", "website": null, "status": "active", "short_abstract": "Acquiring shapes of physical objects in real time and with guaranteed precision to the noise model of the sensor devices.", "abstract": "

The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.

\r\n", "start_date": "2015-12-01", "end_date": "2020-11-30", "leader_id": 193, "logo": { "name": "logo.jpg", "path": "project:ShapeAcquisition", "type": "image/jpeg", "size": 20004, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "P24600-N23", "comment": null } ], "research_areas": [ "Geometry" ], "publications": [ "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "forsythe-2016-ccm", "grossmann-2016-baa", "gruber_horst-2019-baa", "koeppel-2016-baa", "novak_martin-2018-baa", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger-2016-sgp", "pointner_michael-2017-baa", "Radwan_2021_Occ", "Radwan-2017-Occ", "reinwald-2017-baa", "schaffarz_2018-pra", "schartmueller_2018_baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/", "__class": "Publication" }, { "id": "Radwan-2014-CDR", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Efficient Collision Detection While Rendering Dynamic Point Clouds", "date": "2014-05", "abstract": "A recent trend in interactive environments is the use of unstructured\nand temporally varying point clouds. This is driven by both\naffordable depth cameras and augmented reality simulations. One\nresearch question is how to perform collision detection on such\npoint clouds. State-of-the-art methods for collision detection create\na spatial hierarchy in order to capture dynamic point cloud surfaces,\nbut they require O(NlogN) time for N points. We propose\na novel screen-space representation for point clouds which exploits\nthe property of the underlying surface being 2D. In order for dimensionality\nreduction, a 3D point cloud is converted into a series\nof thickened layered depth images. This data structure can be constructed\nin O(N) time and allows for fast surface queries due to\nits increased compactness and memory coherency. On top of that,\nparts of its construction come for free since they are already handled\nby the rendering pipeline. As an application we demonstrate\nonline collision detection between dynamic point clouds. It shows\nsuperior accuracy when compared to other methods and robustness\nto sensor noise since uncertainty is hidden by the thickened boundary.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1024, "image_height": 1024, "name": "Radwan-2014-CDR-.jpg", "type": "image/jpeg", "size": 1617713, "path": "Publication:Radwan-2014-CDR", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 923, "drupal_id": 281, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/MohamedRadwan", "firstname": "Mohamed", "lastname": "Radwan", "searchname": "Mohamed Radwan", "is_teacher": false, "name": "Mohamed Radwan", "publications": [ "Kan_Peter-2021-MDPI", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "Doerflinger-2016-CDV", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Radwan_2021_Occ", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "Radwan-2017-Occ", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=923", "__class": "Author" }, { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], "publication_field": [ { "publ_id": "antes-2026-maa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "ausserer-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "balvan-rsf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "beleznai_csaba-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "belic_dejan_pr", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bodzenta-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "brenner_simon-2013-ba", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "csecsy-fcd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "darovskikh_leonid-2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "deak-a3", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "depner_dennis-2020-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "depner_dennis-2022-maa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "elagrod_ahmed-baa", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "fitzinger_andreas-daa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "fraissl_niklas-2023-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "fuchs_peter-2024-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "fuericht_2025_prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "fuericht_elias-2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "geyer_lukas_2015_prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "geyer_lukas-2014-bat", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "giefing_juergen-2013-ba", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "grossmann-2016-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "gruber_horst-2019-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "gruber_horst-2019-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "hamzic_ademir-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "harisch-mvs", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "hatzinger-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { 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{ "publ_id": "micu_2025-prj", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "miklautsch_florian-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "mueller-2024-rbt", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "mujadzic_iman_baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "mujadzic-2023-hfp", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "nawrocki-2024-nsr", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "negi_ashish-2023-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "neumann-sfd", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "novak_martin-2018-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "ohrhallinger_stefan-2013-c2d", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "ohrhallinger_stefan-2013-smi", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "ohrhallinger_stefan-2018-cgf", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": 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"proelss-bdp", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "proelss-bdp", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "prohaska_julia-2023-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "Radwan-2014-CDR", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "rait_alexius-2021-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "reiher_joerg-2023-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "reiher-joerg-2024-daa", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "reinwald-2017-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "rozanics-lsf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "rubik_michael-2022-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "schaffarz_2018-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "schartmueller_2018_baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "schefcik_juergen_2015_baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "schmid_andreas_2013-prj", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "schoerkhuber_dominik-2016-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "schoerkhuber_dominik-2016-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "schrammel_sebastian_baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "schuetz_markus-2013-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "schulz-faf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "seisenbacher-cyclesafely", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "slajcho_levente-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "slavtchev-dcd", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "starnberger-cli", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "steinheber-2024-oto", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "steinschauer-2020-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "trenovatz-lpd", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "trenovatz-lpd", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "tumpach_barbara-pra", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "vasiljevic-gpc", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "wiesinger_2025-baa", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "wiesinger_klemens-2023-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wildt-tci", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "withalm_brigitte-2022-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wolffhardt-2026-prj", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "yoo-chaeran-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "zeilinger_matthias-2020-baa", "field_id": "co_supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=948", "__class": "Author" }, { 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The current acquisition pipeline for visual models of 3D worlds is based on a paradigm of planning a goal-oriented acquisition - sampling on site - processing. The digital model of an artifact (an object, a building,up to an entire city) is produced by planning a specific scanning campaign, carefully selecting the (often costly) acquisition devices, performing the on-site acquisition at the required resolution and then post-processing the acquired data to produce a beautified triangulated and textured model. However, in the future we will be faced with the ubiquitous availability of sensing devices that deliver different data streams that need to be processed and displayed in a new way, for example smartphones, commodity stereo cameras, cheap aerial data acquisition devices, etc. We therefore propose a radical paradigm change in acquisition and processing technology: instead of a goal-driven acquisition that determines the devices and sensors, we let the sensors and resulting available data determine the acquisition process. Data acquisition might become incidental to other tasks that evices/people to which sensors are attached carry out. A variety of challenging problems need to be solved to exploit this huge amount of data, including: dealing with continuous streams of time-dependent data, finding means of integrating data from different sensors and modalities, detecting changes in data sets to create 4D models, harvesting data to go beyond simple 3D geometry, and researching new paradigms for interactive inspection capabilities with 4D data sets. In this project, we envision solutions to these challenges, paving the way for affordable and innovative uses of information technology in an evolving world sampled by ubiquitous visual sensors. Our approach is high-risk and an enabling factor for future visual applications. The focus is clearly on basic research questions to lay the foundation for the new paradigm of incidental 4D data capture.

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"Zezulka-2024-FTS", "Zezulka-2025-NRL", "Zezulka-2026-FGTS", "Zezulka-2026-GridThermalSim", "zhang_le-2010-IGA", "zhang-thesis", "ZL_2011_IVIG", "zotti-2001-dip", "zotti-2003-ash", "zotti-2005-lum", "Zotti-2005-vis", "zotti-2006-dgm", "zotti-2006-pla", "zotti-2007-eg", "zotti-2007-hdr", "zotti-2007-PhD", "zotti-2007-wscg", "ZOTTI-2016-VAA", "ZOTTI-2017-BM", "ZOTTI-2017-TSP", "zsolnai-2018-gms", "zsolnai-feher-thesis-2019", "zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2013/ohrhallinger_stefan-2013-c2d/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2013-smi", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Minimizing Edge Length to Connect Sparsely Sampled Unorganized Point Sets", "date": "2013-10", "abstract": "Most methods for interpolating unstructured point clouds handle densely sampled point sets quite well but get into trouble when\nthe point set contains regions with much sparser sampling, a situation often encountered in practice. In this paper, we present a new\nmethod that provides a better interpolation of sparsely sampled features.\nWe pose the surface construction problem as finding the triangle mesh which minimizes the sum of all triangles’ longest edge.\nThe output is a closed manifold triangulated surface Bmin. Exact computation of Bmin for sparse sampling is most probably NP-hard, and therefore we introduce suitable heuristics for its computing. The algorithm first connects the points by triangles chosen\nin order of their longest edge and with the requirement that all edges must have at least 2 incident triangles. This yields a closed\nnon-manifold shape which we call the Boundary Complex. Then we transform it into a manifold triangulation using topological\noperations. We show that in practice, runtime is linear to that of the Delaunay triangulation of the points.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 380, "image_height": 217, "name": "ohrhallinger_stefan-2013-smi-image.png", "type": "image/png", "size": 22445, "path": "Publication:ohrhallinger_stefan-2013-smi", "url": "https://www.cg.tuwien.ac.at/research/publications/2013/ohrhallinger_stefan-2013-smi/ohrhallinger_stefan-2013-smi-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2013/ohrhallinger_stefan-2013-smi/ohrhallinger_stefan-2013-smi-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], 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"FREUDE-2015-phd", "freude-2023-prh", "freude-2023-prh", "freude-2023-sem", "freude-2023-sem", "freude-2025-iso", "freude-2025-iso", "freude-2025-ras", "Fritz-2012", "fruehstueck-2008-gpu", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "Fuericht-2026-FPD", "Fuerst_2014_BSc", "Fuerst-2009-dfr", "FUERST-2013-RTWS", "Fuerst-Bachelor-2013", "G_P_06_RFS", "GAHR-2013-AGM", "gamper_2008_or", "GAMPER-2018-OSG", "gamsjaeger-2022-proc", "gangl-2018-cgbase", "GANTNER-2018-ARO", "GANTNER-2019-CRS", "GEBHART-2008-AGV", "gersthofer_2017_COP", "gersthofer-2016-sosob", "geyer_lukas_2015_prj", "geyer_lukas-2014-bat", "geyer-2018-apbf", "giefing_juergen-2013-ba", "GIEGL-2006-QVS", "GIEGL-2007-FVS", "GIEGL-2007-QV1", "GIEGL-2007-UNP", "gilmutdinov-2022-aomlbwug", "glanz-2017-pbrcomparison", "goel-2024-rdr", "goetz_johann-cagmfp", "Goldmann_2011_GPU", "gramegna-rfr", "Grasberger_08_VV", "Grasberger_2008_ISR", "grasberger_2009_CSB", "GRELAUD-2009-EPA", "GRIESHOFER-2018-GOS", "Grisenti-2026-HSHPG", "grosse-2009-avp", "Grossfurtner-2023-paego", "grossmann-2016-baa", "gruber_horst-2019-baa", "Gruber2019", "Guerrero_Paul_2014_EPG", "guerrero-2008-dip", "guerrero-2008-sli", "Guerrero-2014-GRF", "Guerrero-2014-TPS", "guerrero-2015-lsp", "gurtler-2014-ssggm", "Gusenbauer_Matthias_2015_ANM", "Habel_07_xms", "Habel_08_SSH", "Habel_09_PGT", "Habel_09_PGT", "Habel_2007_IAG", "Habel_2007_RTT", "Habel_2009_PhD", "Habel_2012_PSP", "Habel_LSN_2011", "Habel_RAV_2010", "Habel-09-RAT", "Habel-09-THB", "Habel-2010-EIN", "hafner_christian-2013-oflow", "hafner-2015-eigf", "HAFNER-2015-FEM", "hafner-2015-onff", "halbritter-2024-m2m", "hamzic_ademir-baa", "hanko-2019-ani", "HANN_2022_IPT", "harisch-mvs", "Hartl_2006_OgC", "hasbay-2026-tfo", "hatzinger-2026-baa", "hauer_alex-2013-ba", "havran-2005-egsr", "havran-2005-sccg", "havran-2006-tut", "Hebart-2011-ColorAdjustment", "HECHER-2010-3DS", "hecher-2012-MH", "hecher-2014-MH", "HECHER-2017-HDY", "heinz-2019-psi", "heiss-rfr", "herzberger-2021-cmake", "hofer_kerstin-2023-sketch2model", "hoffer-toth-com", "holom_2025-prj", "Holzer-06-HDR", "Honic_2021", "hornacek-2010-evp", "horvath_imp", "houska-2020-IPGM", "HU-2017-ETM", "huber-2025-esl", "huerbe-2019-BA", "HUMMEL-2001-MSE", "Ilcik_2015_LAY", "ilcik-2010-ps", "ilcik-2011-pmous", "ilcik-2013-cipmi", "ilcik-2013-pmsg", "ilcik-2014-cgbpmi", "ilcik-2016-cmssg", "ILCIK-phdthesis", "ilcikova-thesis", "intel2016", "ioannis-2026-sro", "Ishmukhametov_Denis_2011_EIN", "jafari-2023-avk", "jahrmann_klemens_KFR", "JAHRMANN-2013-IGR", "JAHRMANN-2015-GRASS", "JAHRMANN-2016-IGR", "JAHRMANN-2017-RRTG", "jeschke-05-AIP", "jeschke-05-AIP", "jeschke-05-ARI", "jeschke-05-ISTAR", "jeschke-09-praguetalk", "jeschke-09-rendering", "jeschke-09-solver", "Jeschke-2002-LEM", "Jeschke-2002-LEMA", "Jeschke-2002-TDM", "Jeschke-2002-TDMR", "JESCHKE-2007-ISC", "jeschke-2010-diff", "jeschke-2011-est", "jeschke-2011-esttalk", "jeschke-2011-talkPrague", "Jia-thesis", "Jimenez_SSS_2015", "jira_marcel-2024-baa", "JORDAKIEVA-2017-VFR", "KAFKA-2014-19MRD", "kaipel_nikolas-2025-baa", "kapferer_2021_advancing", "karampekios_anastasios-prj", "Kathi-2018-VRB", "kazda-tmd", "Keilman2022", "kendlbacher-2016", "kenzel_michael_2021_cuda", "kerbl_2019_planet_poster", "kerbl_2021_hdg", "kerbl-2020-improvencoding", "kerbl-2022-cuda", "kerbl-2022-trienc", "kimmersdorfer-2021-mzip", "kimmersdorfer-2024-vcpr", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "KLAUS-2020-PCC", "klein-2020-rtgi", "kment_2008_cc", "knecht_2013_RSM", "knecht_martin_2010_DIR", "knecht_martin_2012_BRDFEstimation", "knecht_martin_2012_RSMR", "knecht_martin_2013_ReflRefrObjsMR", "knecht_martin-2011-FPSPAR", "Knecht_Schwaerzler_2008_ash", "knecht-2007-ao", "knecht-2009-MKN", "knecht-2009-TAS", "knecht-2011-CBCM", "knoetzl_alexander-2025-prj", "koch-2019-PR", "KOCH-2021-GVS", "KOCH-2021-GVSDA", "koenigsberger-2024-msu", "koeppel-2016-baa", "Koeppl-2023-DLO", "koessler-2013-BA", "Kogelnig_Philip-2008-OGC", "kohlhas_jakob-2023-pra", "Kollmann-2015-DoF", "komon-2026-rso", "Komon2022", "konrad-2020-baa", "Kovacs_2017", "kovacs-2023-ttm", "kovacs-2025-pf", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "KREUZER-2014-DPA", "KREUZER-2015-DPA", "kreuzer-2016-isf", "KREUZER-2017-PBF", "kristmann-2022-occ", "kristmann-2023-vkl", "KRISTMANN-24-JPG", "kristmann-thesis", "Kroesl_2020_11_09", "Kroesl_Katharina_2016_PPT", "kroesl-2012-rtpm", "kroesl-2017-LiteMaker", "kroesl-2018-DC", "kroesl-2018-TVS", "kroesl-2019-ICthroughVR", "kroesl-2019-MoD", "KROESL-2020-SVI", "kroesl-2020-XREye", "kubicek-2024-baa", "KUE11", "kuehtreiber-2010-ikph", "Kugler-2021", "kusternig-2009-rtr", "laager_florian-2013-camr", "laager_florian-2014-daa", "labschuetz-2010-bms", "labschuetz-2011-rsr", "Lan25", "landauer-2022-kido", "lang_jakob-2022-baa", "Langer_2013_ExactGeom", "lazarevic-tci", "LEHNINGER-2007-SSV", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer_2020-cag", "leimer_2020-cag", "leimer-2013-esopc", "leimer-2014-fsco", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "leimer-2018-sar", "leimer-2023-ecd", "LEOPOLD-2017-ALG", "leopold-2019-vox", "leutschacher_2025_prj", "Liang-2023-TVR", "lipp_markus-2010-DAC", "LIPP-2007-CGA", "LIPP-2008-IEV", "LIPP-2009-PGL", "LIPP-2009-PGL2", "LIPP-2010-PGMS", "lipp-2019-rtxq3", "LIPP-2020-PHD", "lipp-2024-val", "lipp2011a", "liu-2012-tcvd", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "Luksch_2007_RHR", "luksch_2008_asm", "luksch_2020", "LUKSCH-2009-BBC", "LUKSCH-2013-FLM", "LUKSCH-2014-RTR", "LUKSCH-2019-IGI", "Lupu-2026-Cloth", "macho_jonas-2024-baa", "MAESTRI-2012-MRW", "manpreet_kainth-2012-rus", "Mantler-06-landscape", "MANTLER-2007-DMBBC", "marek-2011-normalest", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "marin-thesis", "markovich_aleksandar-2022-maa", "Martinez-2015-TTB", "maschek-2010-rts", "matt2011", "MATTAUSCH-2006-AVC", "mattausch-2007-iav", "MATTAUSCH-2007-OSP", "mattausch-2008-CHC", "mattausch-2010-tao", "mattausch-2010-var", "Mattausch-2010-vcr", "MATTAUSCH-2012-EOV", "MATTAUSCH-2012-TIS", "MATTAUSCH-2013-FSBE", "MATTAUSCH-2015-CHCRT", "mayer_2011_cipa", "MAYER-2010-MTX", "Mayer-2010-VT", "mayr_patrick_maa", "Mayrhauser-2016-Cnc", "MAYRHAUSER-2016-SCA", "mazza-2012-bakk", "Meier_2024_WIALT", "Meier_2024_WIALT", "MeierStauffer-2013-iihf", "metzger-2024-smv", "micu_2025-prj", "miklautsch_florian-baa", "MOELLINGER-2010-SD", "MORARU-2014-CPD", "mortezapoor-2022-photogrammabot", "Mortezapoor2021ssc", "Muehlbacher_2012_RRM", "Muehlbacher_2012_RTR", "MuehlbacherArbesser-2009-LMDLightcuts", "MUEHLBERGER-2010-AFSM", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "murturi_PGG", "musialski_2012_aachen", "musialski_2012_fice", "musialski_2016_sosp", "musialski-2012-icb", "musialski-2012-sur", "Musialski-2013-ipmum", "musialski-2013-prag", "musialski-2013-surcgf", "musialski-2015-ista", "musialski-2015-pixel", "musialski-2015-souos", "musialski-2015-vrvis", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "Neum03CAMR", "Neum04GCM", "neumann-2004-aip", "Neumann-2004-HOB", "neumann-2004-ipm", "neumann-2006-gamma", "neumann-sfd", "Niederreiter_2011_SM", "NIEDERREITER-2010-SIM", "NK_2011", "novak_martin-2018-baa", "Nuernberg-2011-CBO", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-stefan-2012-the", "OPPITZ-2017-3DM", "oppitz-2023-ais", "OPPTIZ-2018-IVRL", "OTEPKA-2020-PPC", "ott-rgi", "ott-va", "Pajer-2006-tmcg", "pakkanen-2026-prj", "panfili-2019-VAVR", "panfili-2021-myop", "pangerl_2013_GPD", "papantonakis-2024-rmf", "parakkat-2024-ballmerge", "parakkat-2024-ballmerge", "PARZER-2010-STELL", "PB-VRVis-2018-005", "pegrisch_2018_uvs", "Pel-2026", "PERNDORFER-2015-ECS", "pernsteiner-2022-msh", "pernsteinre_jakob_2020_eechc", "Petersen_Viktoria-2024-SBO", "pezenka_lukas2012-bspcga", "pichler_2022_fro", "pichler_2022_fro", "PIRINGER-2003-HBOQ", "pirraku-f", "piskernik-2010-eud", "pistellato-2023-elp", "PLUCH-2009-ANP", "PODARAS-2017-ACRS", "Podaras-praktikum-2016", "pogrzebacz-2014-gggm", "pogrzebacz-2023-vkl", "pointner_michael-2017-baa", "pointner_simon-2022-maa", "POLLAK-2016-STEREO", "polzer-sog", "pr10", "praeauer-2011-et", "Prast_Stefanie_2013-CLG", "preiner_2010_GIPC", "preiner_2012_AS", "preiner_2012_AS", "Preiner_2019", "PREINER-2009-GIPC", "preiner11IR", "preiner2014clop", "preissegger-2025-vrh", "Preymann-2022-pytamashii", "prieler_11_patchmatch", "prieler_daniel-2013-da", "prieler_daniel-2013-pra", "probst_kolesik_2010_pk10", "probst-2023-para", "proelss-bdp", "prohaska_julia-2023-baa", "prost-2016-molecule", "PROST-2019-RTPAL", "Przemyslaw_Gora_2016_UVU", "PUEYO-2019-SCL", "Purgathofer_2012_IVA", "purgathofer-1988-simple", "purgathofer-2004-asp", "purgathofer-2004-comp", "purgathofer-2004-neue", "purgathofer-2004-real", "purgathofer-2004-some", "Purgathofer-2005-Ros", "Purgathofer-2005-Tub", "Purgathofer-2006-VRVis", "Purgathofer-2009-rus", "purgathofer-2010-cgtrends", "Purgathofer-2014-EurasiaGr", "radax-2008-ir", "RADAX-2010-LOD", "radax-2010-RTL", "Radax2008", "radits_2011_gahdr", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis", "Rainer_2017", "rait_alexius-2021-baa", "Rait2022", "rasch_martina-2013-HDRImage", "rasoulzadeh-2022-strokes2surface", "rasoulzadeh-2024-strokes2surface", "RAUCH-2011-TMS", "recheis-2009-arr", "REICHHOLF-2023-HRO", "Reichinger-12", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "Reimerth_Saskia_2016_RGU", "REINALTER_2010_VIS", "reinwald-2017-baa", "reisinger-2023-iad", "Reisner_2013_RSB", "Reisner_Irene_2013_R3D", "reitsamer-2008-tsud", "reznicek-2020-fpgaray", "richter-2020-ani", "riegler_2020_framework", "roch-2007-rtt", "Roch2006", "Roegner_RHF_2010", "ROEGNER-2014-TUI", "ROEGNER-2015-IBR", "ROESSLER-2012-OGLES", "roth_2021_vdst", "roth_vdi", "roth-2023-vlk", "rozanics-lsf", "rubik_michael-2022-baa", "Rudolf_06_IDM", "rumpelnik_martin_2020_PRM", "rumpelnik_martin_2020_PRM", "Rumpelnik_Martin-2020-NPR", "rumpelnik-2022-vlk", "rumpler_2019_PPC", "Rumpler-2019-PPC", "RUMPLER-2021-VOGL", "rumpler-2023-dcmcm", "S05", "SABADELLO-2006-HEG", "sakai_2013", "SAKAI-2015-EAPI", "sakai-2021-eif", "sakai-2024-asa", "sakai-2025-stater", "sakai-thesis", "santos-2017-dacge", "sari2008mt", "SATZER-2008", "schaffarz_2018-pra", "scharl-master-thesis", "schartmueller_2018_baa", "schaukowitsch-2013-fls", "SCHAUKOWITSCH-2015-REMOTE", "SCHAUKOWITSCH-2015-STELL", "schedl-2012-dof", "schedl-2013-gP4", "schedl-thesis", "schefcik_juergen_2015_baa", "Scheiblauer-2006-DA", "Scheiblauer-2008-DCW", "SCHEIBLAUER-2009-IDCE", "scheiblauer-2011-cag", "scheiblauer-2011-chnt", "scheiblauer-2012-chnt", "scheiblauer-2013-wscg", "SCHEIBLAUER-2015-WFC", "scheiblauer-thesis", "scheiblauer-thesis", "scherzer_2010a", "Scherzer-2007-PCS", "SCHERZER-2008-FSR", "scherzer-2009-isvc", "SCHERZER-2012-TCM", "scherzer-eg2010", "scherzer-thesis", "scherzer2010b", "scherzer2010d", "scherzer2010e", "scherzer2011c", "scherzer2011d", "schmid_andreas_2013-prj", "schneider-sturm-2026-mia", "schober_florian_2012_aspm", "schoerkhuber_dominik-2016-baa", "scholz_2017_bac", "schrammel_sebastian_baa", "schuetz_markus-2013-pra", "SCHUETZ-2014-RRLP", "SCHUETZ-2015-HQP", "SCHUETZ-2016-POT", "schuetz-2018-PPC", "schuetz-2019-CLOD", "schuetz-2019-LCO", "SCHUETZ-2020-MPC", "schuetz-2020-PPC", "SCHUETZ-2021-DISS", "SCHUETZ-2021-PCC", "SCHUETZ-2021-PCC", "SCHUETZ-2022-PCC", "SCHUETZ-2023-LOD", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schulz-faf", "schwaerzler_2018_phd", "schwaerzler-2009-ass", "SCHWAERZLER-2012-FAS", "SCHWAERZLER-2013-FPCSS", "SCHWAERZLER-2017-SBGM", "schwarz-2024-m2m", "Schwengerer_Mathias-2022-BGL", "seczer-2025-vrs", "seidemann-bachelor-2013", "seisenbacher-cyclesafely", "Selig_2005_WRO", "Selig-2007-VegAnim", "Semmelrath-2022-IRS", "seyr-2025-lbt", "SIETZEN-2017-BBO", "silvana_2014", "sippl-2014-fss", "slajcho_levente-baa", "slavtchev-dcd", "Spechtenhauser_Florian_2013_SIU", "spelitz_stefan-2012-CDTFMR", "sperl-2013-BA", "Sperl-praktikum-2016", "spielmann-2006-ner", "spitaler-2015-pbi", "spitaler-afr-2011", "SSMW09", "stappen_SteFAS", "stappen-2019-vrs", "starnberger-cli", "stauss-2025-gfd", "steiner_2016_isad", "steiner_2016_isad", "STEINER-2006-SVC", "steiner-2013-BS", "steiner-2014-da", "steinheber-2024-oto", "steinkellner-2022-dll", "steinkellner-2024-dll", "STEINLECHNER-2019-APS", "steinschauer-2020-baa", "steinschorn-2023-p2m", "steinschorn-2023-p2m", "steinschorn-2025-par", "steinschorn-2025-parameter", "Stingl_2011_RHS", "STINGL-2009-RHS", "Stoll2020", "STREICHER-2022-DSLS", "Streicher-bachelor-2013", "STU-SR-25", "sturl_andreas-caflm", "STURN-2013-LGI", "STURN-2013-LSI", "Sturn-2013-ST", "sturn-thesis", "stutter_david-2012-kft", "subtil-2006-disp", "SUNDSTEDT-2008-ASF", "sundstedt-2013-vag", "szabo_2013_ssrr", "Tanzmeister_2011_I3D", "tatzgern-2020-sst", "Thann_2019_01", "themmer-2018-fbx", "thurner-2019-ani", "TK_2011", "tobler02_amrmg", "tobler02_mbpls", "tobler97_hsdasr", "TOMASCHITZ-2020-EP", "TOROMANOFF-2005-ASA", "TR 193-02-2019-4", "TR-186-2-02-04", "TR-186-2-05-05", "TR-186-2-06-02", "TR-186-2-07-01", "TR-186-2-07-09", "TR-186-2-08-09", "TR-186-2-12-01", "Tragust-2012-GMHPC", "Tragust-2014-master-thesis", "traxler-1997-TRA", "trenkwalder-2008-landscape", "trenkwalder-2013-ma", "trenovatz-lpd", "trumpf_stefan_2012", "Tucek_2018", "Tucek_Tom-2016-aai", "tumpach_barbara-pra", "ulschmid-2024-reo", "ulschmid-2025-apc", "ulschmid-phdthesis", "unterguggenberger-2010-cmph", "unterguggenberger-2020-fmvr", "unterguggenberger-2021-msh", "unterguggenberger-2022-vulkan", "unterguggenberger-2023-vaw", "unterguggenberger-2024-fropo", "unterguggenberger-phdthesis", "VASCO-2019-EVR", "vasiljevic-gpc", "VASILJEVS-2018-PMPL", "vis-foa", "VOELKL-2013-RTRS", "voglreiter-2023-tro", "Voglsam_2012_RTR", "Voglsam_2013_RRT", "wagner-2024-par", "wallner_2008_ltosd", "WALLNER-2010-CSSAO", "wallner-2015-InfoTheoryPlay", "wallner-2015-ModelingRoutinization", "Wang-2016-BAC", "wang-sifuentes-2018-cgbase", "weber_2008_sh", "WEBER-2012-MIC", "WEBER-2015-PRA", "WEBER-2015-PRA1", "weidlich_2009_REL", "weidlich-2009-dispersion", "weidlich-2009-thesis", "weiner-2010-eud", "weinzierl_2012_iism", "weinzierl_heigl-2017-wh14", "Wenigwieser2007", "werdermann-va", "WESSNER-2019-PCW", "WEYDEMANN-2014-PIC", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "weydemann-praktikum-2016", "wieser-2019-ani", "wiesinger_2020_odpr", "wiesinger_2020_odpr", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wiesinger-2023-gcg", "wildt-tci", "Wilkie-2001-Ori", "Wilkie-2004-AMS", "wilkie-2006-dfs", "wilkie-2009-cc", "Wimmer-1999-FWIa", "Wimmer-1999-FWIb", "Wimmer-2001-PBI", "Wimmer-2001-Poi", "Wimmer-2003-RTE", "Wimmer-2004-LSPM", "Wimmer-2005-HOQ", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2006-PSM", "WIMMER-2006-SIV", "WIMMER-2007-GAR", "WIMMER-2007-RTR", "WIMMER-2008-ART", "WIMMER-2008-CAR", "WIMMER-2008-PVR", "WIMMER-2009-ARTR", "WIMMER-2009-ARTR2", "WIMMER-2009-ARTR3", "WIMMER-2009-CSR", "WIMMER-2009-ITC", "WIMMER-2009-VCCG", "WIMMER-2014-DWNT", "WIMMER-2015-CGFC", "WIMMER-2015-CGMC", "WIMMER-2016-FUTURIS", "WIMMER-2016-HARVEST4D", "WIMMER-2019-CGSG", "WIMMER-2020-ASG", "wimmer-2022-acd", "wimmer-2023-acd", "Win25", "WINKLER-2013-AMO", "Winkler-2017", "WINKLER-2019-PDG", "winklhofer_christoph-2013-RRMR", "winklmueller-aso", "withalm_brigitte-2022-baa", "Woerister_2012_ACS", "wolffhardt-2026-prj", "wonka-2000-VisP", "Wonka-2001-Ins", "Wonka-2001-IV", "Wonka-2003-Ins", "WONKA-2006-GVS", "x-mas-2013", "xmas-2025", "yoo-chaeran-2025-pr", "zapotocky_2013_ma", "ZAU14", "zeilinger_matthias-2020-baa", "Zezulka_Matthias-2023-MatLabOptInterface", "Zezulka-2024-FTS", "Zezulka-2025-NRL", "Zezulka-2026-FGTS", "Zezulka-2026-GridThermalSim", "zhang_le-2010-IGA", "zhang-thesis", "ZL_2011_IVIG", "zotti-2001-dip", "zotti-2003-ash", "zotti-2005-lum", "Zotti-2005-vis", "zotti-2006-dgm", "zotti-2006-pla", "zotti-2007-eg", "zotti-2007-hdr", "zotti-2007-PhD", "zotti-2007-wscg", "ZOTTI-2016-VAA", "ZOTTI-2017-BM", "ZOTTI-2017-TSP", "zsolnai-2018-gms", "zsolnai-feher-thesis-2019", "zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2013/ohrhallinger_stefan-2013-smi/", "__class": "Publication" }, { "id": "ohrhallinger-stefan-2012-the", "type_id": "phdthesis", "tu_id": null, "repositum_id": null, "title": "The Intrinsic Shape of Point Clouds", "date": "2012-07", "abstract": "Given a point cloud, in the form of unorganized points, the problem of automatically connecting the dots to obtain an aesthetically pleasing and piecewise-linear closed interpolating boundary shape has been extensively researched for over three decades. In R3 , it is even more complicated to find an aesthetic closed oriented surface. Most previous methods for shape reconstruction exclusively from coordinates work well only when the point spacing on the shape boundary is dense and locally uniform. The problem of shape construction from non-dense and locally non-uniformly spaced point sets is in our opinion not yet satisfactorily solved. Various extensions to earlier methods do not work that well and do not provide any performance guarantees either.\nOur main thesis in this research is that a point set, even with non-dense and locally non-uniform spacing, has an intrinsic shape which optimizes in some way the Gestalt principles of form perception. This shape can be formally defined as the minimum of an energy function over all possible closed linear piece-wise interpolations of this point set. Further, while finding this optimal shape is NP-hard, it is possible to heuristically search for an acceptable approximation within reasonable time.\nOur minimization objective is guided by Gestalt’s laws of Proximity, Good Continuity and Closure. Minimizing curvature tends to satisfy proximity and good continuity. For computational simplification, we globally minimize the longest-edge-in-simplex, since it is intrinsic to a single facet and also a factor in mean curvature. And we require a closed shape.\nUsing such an intrinsic criterion permits the extraction of an approximate shape with a linearithmic algorithm as a simplicial complex, which we have named the Minimum Boundary Complex. Experiments show that it seems to be a very close approximation to the desired boundary shape and that it retains its genus. Further it can be constructed locally and can also handle sensor data with significant noise. Its quick construction is due to not being restricted by the manifold property, required in the boundary shape. Therefore it has many applications where a manifold shape is not necessary, e.g. visualization, shape retrieval, shadow mapping, and topological data analysis in higher dimensions. The definition of the Minimum Boundary Complex is our first major contribution.\nOur next two contributions include new methods for constructing boundary shapes by transforming the boundary complex into a close approximation of the minimum boundary shape. These algorithms vary a topological constraint to first inflate the boundary complex to recover a manifold hull and then sculpture it to extract a Minimum Boundary approximation, which interpolates all the points. In the R3 method, we show how local minima can be avoided by covering holes in the hull. Finally, we apply a mesh fairing step to optimize mean curvature directly. We present results for shape construction in R2 and R3 , which clearly demonstrate that our methods work better than the best performing earlier methods for non-dense and locally non-uniformly spaced point sets, while maintaining competitive linearithmic complexity.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": "2D and 3D Reconstruction", "filetitle": "thumbnail", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 646, "image_height": 562, "name": "ohrhallinger-stefan-2012-the-thumbnail.jpg", "type": "image/jpeg", "size": 70443, "path": "Publication:ohrhallinger-stefan-2012-the", "url": "https://www.cg.tuwien.ac.at/research/publications/2012/ohrhallinger-stefan-2012-the/ohrhallinger-stefan-2012-the-thumbnail.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2012/ohrhallinger-stefan-2012-the/ohrhallinger-stefan-2012-the-thumbnail:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], "publication_field": [ { "publ_id": "antes-2026-maa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "ausserer-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "balvan-rsf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "beleznai_csaba-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "belic_dejan_pr", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bodzenta-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "brenner_simon-2013-ba", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "csecsy-fcd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "darovskikh_leonid-2025-prj", 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"ordernum": 0 }, { "publ_id": "withalm_brigitte-2022-baa", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wolffhardt-2026-prj", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "yoo-chaeran-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "zeilinger_matthias-2020-baa", "field_id": "co_supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=948", "__class": "Author" } ], "date_end": "2012-07", "date_start": "2006-09", "reviewer_1": [ { "id": 1310, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Dr. F.", "lastname": "Samavati", "searchname": "Dr. F. 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Grogono", "is_teacher": false, "name": "Dr. P. Grogono", "publications": [], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "ohrhallinger-stefan-2012-the", "field_id": "reviewer_2", "ordernum": 1 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1322", "__class": "Author" }, { "id": 1323, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Dr. A.", "lastname": "Agarwal", "searchname": "Dr. A. Agarwal", "is_teacher": false, "name": "Dr. A. Agarwal", "publications": [], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "ohrhallinger-stefan-2012-the", "field_id": "reviewer_2", "ordernum": 2 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1323", "__class": "Author" } ], "rigorosum": "2012-07-12", "supervisor": [ { "id": 1024, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Sudhir", "lastname": "Mudur", "searchname": "Sudhir Mudur", "is_teacher": false, "name": "Sudhir Mudur", "publications": [ "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "ohrhallinger-stefan-2012-the", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1024", "__class": "Author" } ], "research_areas": [ { "id": "Geometry", "drupal_id": 4129, "name": "Geometry Processing", "short_abstract": "Uses concepts from applied mathematics and computer science to design efficient algorithms for the reconstruction, analysis, manipulation, simulation and transmission of complex 3D models. Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. 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"withalm_brigitte-2022-baa", "Woerister_2012_ACS", "wolffhardt-2026-prj", "wonka-2000-VisP", "Wonka-2001-Ins", "Wonka-2001-IV", "Wonka-2003-Ins", "WONKA-2006-GVS", "x-mas-2013", "xmas-2025", "yoo-chaeran-2025-pr", "zapotocky_2013_ma", "ZAU14", "zeilinger_matthias-2020-baa", "Zezulka_Matthias-2023-MatLabOptInterface", "Zezulka-2024-FTS", "Zezulka-2025-NRL", "Zezulka-2026-FGTS", "Zezulka-2026-GridThermalSim", "zhang_le-2010-IGA", "zhang-thesis", "ZL_2011_IVIG", "zotti-2001-dip", "zotti-2003-ash", "zotti-2005-lum", "Zotti-2005-vis", "zotti-2006-dgm", "zotti-2006-pla", "zotti-2007-eg", "zotti-2007-hdr", "zotti-2007-PhD", "zotti-2007-wscg", "ZOTTI-2016-VAA", "ZOTTI-2017-BM", "ZOTTI-2017-TSP", "zsolnai-2018-gms", "zsolnai-feher-thesis-2019", "zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2012/ohrhallinger-stefan-2012-the/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2012-mle", "type_id": "poster", "tu_id": null, "repositum_id": null, "title": "Minimising Longest Edge for Closed Surface Construction from Unorganised 3D Point Sets", "date": "2012-05", "abstract": "Given an unorganised 3D point set with just coordinate data, we formulate the problem of closed surface construction as one requiring minimisation of longest edge in triangles, a criterion derivable from Gestalt laws for shape perception. Next we define the Minimum Boundary Complex (BCmin ), which resembles the desired surface Bmin considerably, by slightly relaxing the topological constraint to make it at least two triangles per edge instead of exactly two required by Bmin . A close approximation of BCmin can be computed fast using a greedy algorithm. This provides a very good starting shape which can be transformed by a few steps into the desired shape, close to Bmin. Our method runs in O(n log n) time, with Delaunay Graph construction as largest run-time factor. 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"zhang_le-2010-IGA", "zhang-thesis", "ZL_2011_IVIG", "zotti-2001-dip", "zotti-2003-ash", "zotti-2005-lum", "Zotti-2005-vis", "zotti-2006-dgm", "zotti-2006-pla", "zotti-2007-eg", "zotti-2007-hdr", "zotti-2007-PhD", "zotti-2007-wscg", "ZOTTI-2016-VAA", "ZOTTI-2017-BM", "ZOTTI-2017-TSP", "zsolnai-2018-gms", "zsolnai-feher-thesis-2019", "zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2012/ohrhallinger_stefan-2012-mle/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2011-001", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Interpolating an unorganized 2D point cloud with a single closed shape", "date": "2011-01", "abstract": "Given an unorganized two-dimensional point cloud, we address the problem of efficiently constructing a single aesthetically pleasing closed interpolating shape, without requiring dense or uniform spacing. Using Gestalt’s laws of proximity, closure and good continuity as guidance for visual aesthetics, we require that our constructed shape be a minimal perimeter, non-self intersecting manifold. We find that this yields visually pleasing results. Our algorithm is distinct from earlier shape reconstruction approaches, in that it exploits the overlap between the desired shape and a related minimal graph, the Euclidean Minimum Spanning Tree (EMST). Our algorithm segments the EMST to retain as much of it as required and then locally partitions and solves the problem efficiently. Comparison with some of the best currently known solutions shows that our algorithm yields better results.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "thumbnail", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 856, "image_height": 370, "name": "ohrhallinger_stefan-2011-001-thumbnail.png", "type": "image/png", "size": 93144, "path": "Publication:ohrhallinger_stefan-2011-001", "url": "https://www.cg.tuwien.ac.at/research/publications/2011/ohrhallinger_stefan-2011-001/ohrhallinger_stefan-2011-001-thumbnail.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2011/ohrhallinger_stefan-2011-001/ohrhallinger_stefan-2011-001-thumbnail:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], 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"depner_dennis-2022-maa", "Derflinger-2024-RTTW", "Derflinger-2026-RTTW2", "Devlin-2002-STA", "dodik-2017-pcbpt", "Doerflinger-2016-CDV", "Dogangonul-2013-sparkGL", "druml-2010-PFL", "Druml-2013-3DrC", "dusberger-2010-msm", "ecormier-nocca-2025-sls", "eibens-2021-tsgpmg", "eibensteiner-2016-pmg", "eibensteiner-2021-pmcg", "eickmeyer-2027-tcd", "EISEMANN-2011-RTS", "EISEMANN-2012-ERT", "EISEMANN-2013-ERT", "elagrod_ahmed-baa", "Erhardt_2025_Scutes", "Erhardt_Seirlehner-2024-TamashiiIntegration", "erler_2024_ppsurf", "erler_2024_ppsurf", "erler_2024_ppsurf", "erler_2024_ppsurf", "erler_philipp-2016-femfluid-prakt", "erler_philipp-2017-phd", "erler_philipp-2017-phd", "erler_philipp-2017-phd", "ERLER-2017-HVR", "erler-2020-p2s", "erler-2020-p2s", "erler-2020-p2s", "erler-2025-lidarscout", "ERNST-2010-IOOC", "ernst-2013-fif", "ESBERGER-2016-AHO", "ESCHNER-2019-GDT", "fan-2014-scfl", "fan-2014-scfl", "Fauster-06-st", "Felberbauer_2012_AG", "fiedler-2009-php", "fiedler-2022-vlk", "fink-2012-cg1", "fink-2013-cag", "fink-2013-gvp", "Fischer-2018-sssvr", "fitzinger_andreas-daa", "fizimayer-2007-art", "fleiss-2015-da", "forsythe-2016-ccm", "FRAISS-2017-PCU", "fraiss-2019-rtx", "FRAISS-2022-CGMM", "fraissl_niklas-2023-pra", "freude_2020_rs", "Freude_MSc", "FREUDE-2015-phd", "freude-2023-prh", "freude-2023-prh", "freude-2023-sem", "freude-2023-sem", "freude-2025-iso", "freude-2025-iso", "freude-2025-ras", "Fritz-2012", "fruehstueck-2008-gpu", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "Fuericht-2026-FPD", "Fuerst_2014_BSc", "Fuerst-2009-dfr", "FUERST-2013-RTWS", "Fuerst-Bachelor-2013", "G_P_06_RFS", "GAHR-2013-AGM", "gamper_2008_or", "GAMPER-2018-OSG", "gamsjaeger-2022-proc", "gangl-2018-cgbase", "GANTNER-2018-ARO", "GANTNER-2019-CRS", "GEBHART-2008-AGV", "gersthofer_2017_COP", "gersthofer-2016-sosob", "geyer_lukas_2015_prj", "geyer_lukas-2014-bat", "geyer-2018-apbf", "giefing_juergen-2013-ba", "GIEGL-2006-QVS", "GIEGL-2007-FVS", "GIEGL-2007-QV1", "GIEGL-2007-UNP", "gilmutdinov-2022-aomlbwug", "glanz-2017-pbrcomparison", "goel-2024-rdr", "goetz_johann-cagmfp", "Goldmann_2011_GPU", "gramegna-rfr", "Grasberger_08_VV", "Grasberger_2008_ISR", "grasberger_2009_CSB", "GRELAUD-2009-EPA", "GRIESHOFER-2018-GOS", "Grisenti-2026-HSHPG", "grosse-2009-avp", "Grossfurtner-2023-paego", "grossmann-2016-baa", "gruber_horst-2019-baa", "Gruber2019", "Guerrero_Paul_2014_EPG", "guerrero-2008-dip", "guerrero-2008-sli", "Guerrero-2014-GRF", "Guerrero-2014-TPS", "guerrero-2015-lsp", "gurtler-2014-ssggm", "Gusenbauer_Matthias_2015_ANM", "Habel_07_xms", "Habel_08_SSH", "Habel_09_PGT", "Habel_09_PGT", "Habel_2007_IAG", "Habel_2007_RTT", "Habel_2009_PhD", "Habel_2012_PSP", "Habel_LSN_2011", "Habel_RAV_2010", "Habel-09-RAT", "Habel-09-THB", "Habel-2010-EIN", "hafner_christian-2013-oflow", "hafner-2015-eigf", "HAFNER-2015-FEM", "hafner-2015-onff", "halbritter-2024-m2m", "hamzic_ademir-baa", "hanko-2019-ani", "HANN_2022_IPT", "harisch-mvs", "Hartl_2006_OgC", "hasbay-2026-tfo", "hatzinger-2026-baa", "hauer_alex-2013-ba", "havran-2005-egsr", "havran-2005-sccg", "havran-2006-tut", "Hebart-2011-ColorAdjustment", "HECHER-2010-3DS", "hecher-2012-MH", "hecher-2014-MH", "HECHER-2017-HDY", "heinz-2019-psi", "heiss-rfr", "herzberger-2021-cmake", "hofer_kerstin-2023-sketch2model", "hoffer-toth-com", "holom_2025-prj", "Holzer-06-HDR", "Honic_2021", "hornacek-2010-evp", "horvath_imp", "houska-2020-IPGM", "HU-2017-ETM", "huber-2025-esl", "huerbe-2019-BA", "HUMMEL-2001-MSE", "Ilcik_2015_LAY", "ilcik-2010-ps", "ilcik-2011-pmous", "ilcik-2013-cipmi", "ilcik-2013-pmsg", "ilcik-2014-cgbpmi", "ilcik-2016-cmssg", "ILCIK-phdthesis", "ilcikova-thesis", "intel2016", "ioannis-2026-sro", "Ishmukhametov_Denis_2011_EIN", "jafari-2023-avk", "jahrmann_klemens_KFR", "JAHRMANN-2013-IGR", "JAHRMANN-2015-GRASS", "JAHRMANN-2016-IGR", "JAHRMANN-2017-RRTG", "jeschke-05-AIP", "jeschke-05-AIP", "jeschke-05-ARI", "jeschke-05-ISTAR", "jeschke-09-praguetalk", "jeschke-09-rendering", "jeschke-09-solver", "Jeschke-2002-LEM", "Jeschke-2002-LEMA", "Jeschke-2002-TDM", "Jeschke-2002-TDMR", "JESCHKE-2007-ISC", "jeschke-2010-diff", "jeschke-2011-est", "jeschke-2011-esttalk", "jeschke-2011-talkPrague", "Jia-thesis", "Jimenez_SSS_2015", "jira_marcel-2024-baa", "JORDAKIEVA-2017-VFR", "KAFKA-2014-19MRD", "kaipel_nikolas-2025-baa", "kapferer_2021_advancing", "karampekios_anastasios-prj", "Kathi-2018-VRB", "kazda-tmd", "Keilman2022", "kendlbacher-2016", "kenzel_michael_2021_cuda", "kerbl_2019_planet_poster", "kerbl_2021_hdg", "kerbl-2020-improvencoding", "kerbl-2022-cuda", "kerbl-2022-trienc", "kimmersdorfer-2021-mzip", "kimmersdorfer-2024-vcpr", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "KLAUS-2020-PCC", "klein-2020-rtgi", "kment_2008_cc", "knecht_2013_RSM", "knecht_martin_2010_DIR", "knecht_martin_2012_BRDFEstimation", "knecht_martin_2012_RSMR", "knecht_martin_2013_ReflRefrObjsMR", "knecht_martin-2011-FPSPAR", "Knecht_Schwaerzler_2008_ash", "knecht-2007-ao", "knecht-2009-MKN", "knecht-2009-TAS", "knecht-2011-CBCM", "knoetzl_alexander-2025-prj", "koch-2019-PR", "KOCH-2021-GVS", "KOCH-2021-GVSDA", "koenigsberger-2024-msu", "koeppel-2016-baa", "Koeppl-2023-DLO", "koessler-2013-BA", "Kogelnig_Philip-2008-OGC", "kohlhas_jakob-2023-pra", "Kollmann-2015-DoF", "komon-2026-rso", "Komon2022", "konrad-2020-baa", "Kovacs_2017", "kovacs-2023-ttm", "kovacs-2025-pf", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "KREUZER-2014-DPA", "KREUZER-2015-DPA", "kreuzer-2016-isf", "KREUZER-2017-PBF", "kristmann-2022-occ", "kristmann-2023-vkl", "KRISTMANN-24-JPG", "kristmann-thesis", "Kroesl_2020_11_09", "Kroesl_Katharina_2016_PPT", "kroesl-2012-rtpm", "kroesl-2017-LiteMaker", "kroesl-2018-DC", "kroesl-2018-TVS", "kroesl-2019-ICthroughVR", "kroesl-2019-MoD", "KROESL-2020-SVI", "kroesl-2020-XREye", "kubicek-2024-baa", "KUE11", "kuehtreiber-2010-ikph", "Kugler-2021", "kusternig-2009-rtr", "laager_florian-2013-camr", "laager_florian-2014-daa", "labschuetz-2010-bms", "labschuetz-2011-rsr", "Lan25", "landauer-2022-kido", "lang_jakob-2022-baa", "Langer_2013_ExactGeom", "lazarevic-tci", "LEHNINGER-2007-SSV", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer_2020-cag", "leimer_2020-cag", "leimer-2013-esopc", "leimer-2014-fsco", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "leimer-2018-sar", "leimer-2023-ecd", "LEOPOLD-2017-ALG", "leopold-2019-vox", "leutschacher_2025_prj", "Liang-2023-TVR", "lipp_markus-2010-DAC", "LIPP-2007-CGA", "LIPP-2008-IEV", "LIPP-2009-PGL", "LIPP-2009-PGL2", "LIPP-2010-PGMS", "lipp-2019-rtxq3", "LIPP-2020-PHD", "lipp-2024-val", "lipp2011a", "liu-2012-tcvd", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "Luksch_2007_RHR", "luksch_2008_asm", "luksch_2020", "LUKSCH-2009-BBC", "LUKSCH-2013-FLM", "LUKSCH-2014-RTR", "LUKSCH-2019-IGI", "Lupu-2026-Cloth", "macho_jonas-2024-baa", "MAESTRI-2012-MRW", "manpreet_kainth-2012-rus", "Mantler-06-landscape", "MANTLER-2007-DMBBC", "marek-2011-normalest", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "marin-thesis", "markovich_aleksandar-2022-maa", "Martinez-2015-TTB", "maschek-2010-rts", "matt2011", "MATTAUSCH-2006-AVC", "mattausch-2007-iav", "MATTAUSCH-2007-OSP", "mattausch-2008-CHC", "mattausch-2010-tao", "mattausch-2010-var", "Mattausch-2010-vcr", "MATTAUSCH-2012-EOV", "MATTAUSCH-2012-TIS", "MATTAUSCH-2013-FSBE", "MATTAUSCH-2015-CHCRT", "mayer_2011_cipa", "MAYER-2010-MTX", "Mayer-2010-VT", "mayr_patrick_maa", "Mayrhauser-2016-Cnc", "MAYRHAUSER-2016-SCA", "mazza-2012-bakk", "Meier_2024_WIALT", "Meier_2024_WIALT", "MeierStauffer-2013-iihf", "metzger-2024-smv", "micu_2025-prj", "miklautsch_florian-baa", "MOELLINGER-2010-SD", "MORARU-2014-CPD", "mortezapoor-2022-photogrammabot", "Mortezapoor2021ssc", "Muehlbacher_2012_RRM", "Muehlbacher_2012_RTR", "MuehlbacherArbesser-2009-LMDLightcuts", "MUEHLBERGER-2010-AFSM", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "murturi_PGG", "musialski_2012_aachen", "musialski_2012_fice", "musialski_2016_sosp", "musialski-2012-icb", "musialski-2012-sur", "Musialski-2013-ipmum", "musialski-2013-prag", "musialski-2013-surcgf", "musialski-2015-ista", "musialski-2015-pixel", "musialski-2015-souos", "musialski-2015-vrvis", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "Neum03CAMR", "Neum04GCM", "neumann-2004-aip", "Neumann-2004-HOB", "neumann-2004-ipm", "neumann-2006-gamma", "neumann-sfd", "Niederreiter_2011_SM", "NIEDERREITER-2010-SIM", "NK_2011", "novak_martin-2018-baa", "Nuernberg-2011-CBO", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-stefan-2012-the", "OPPITZ-2017-3DM", "oppitz-2023-ais", "OPPTIZ-2018-IVRL", "OTEPKA-2020-PPC", "ott-rgi", "ott-va", "Pajer-2006-tmcg", "pakkanen-2026-prj", "panfili-2019-VAVR", "panfili-2021-myop", "pangerl_2013_GPD", "papantonakis-2024-rmf", "parakkat-2024-ballmerge", "parakkat-2024-ballmerge", "PARZER-2010-STELL", "PB-VRVis-2018-005", "pegrisch_2018_uvs", "Pel-2026", "PERNDORFER-2015-ECS", "pernsteiner-2022-msh", "pernsteinre_jakob_2020_eechc", "Petersen_Viktoria-2024-SBO", "pezenka_lukas2012-bspcga", "pichler_2022_fro", "pichler_2022_fro", "PIRINGER-2003-HBOQ", "pirraku-f", "piskernik-2010-eud", "pistellato-2023-elp", "PLUCH-2009-ANP", "PODARAS-2017-ACRS", "Podaras-praktikum-2016", "pogrzebacz-2014-gggm", "pogrzebacz-2023-vkl", "pointner_michael-2017-baa", "pointner_simon-2022-maa", "POLLAK-2016-STEREO", "polzer-sog", "pr10", "praeauer-2011-et", "Prast_Stefanie_2013-CLG", "preiner_2010_GIPC", "preiner_2012_AS", "preiner_2012_AS", "Preiner_2019", "PREINER-2009-GIPC", "preiner11IR", "preiner2014clop", "preissegger-2025-vrh", "Preymann-2022-pytamashii", "prieler_11_patchmatch", "prieler_daniel-2013-da", "prieler_daniel-2013-pra", "probst_kolesik_2010_pk10", "probst-2023-para", "proelss-bdp", "prohaska_julia-2023-baa", "prost-2016-molecule", "PROST-2019-RTPAL", "Przemyslaw_Gora_2016_UVU", "PUEYO-2019-SCL", "Purgathofer_2012_IVA", "purgathofer-1988-simple", "purgathofer-2004-asp", "purgathofer-2004-comp", "purgathofer-2004-neue", "purgathofer-2004-real", "purgathofer-2004-some", "Purgathofer-2005-Ros", "Purgathofer-2005-Tub", "Purgathofer-2006-VRVis", "Purgathofer-2009-rus", "purgathofer-2010-cgtrends", "Purgathofer-2014-EurasiaGr", "radax-2008-ir", "RADAX-2010-LOD", "radax-2010-RTL", "Radax2008", "radits_2011_gahdr", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis", "Rainer_2017", "rait_alexius-2021-baa", "Rait2022", "rasch_martina-2013-HDRImage", "rasoulzadeh-2022-strokes2surface", "rasoulzadeh-2024-strokes2surface", "RAUCH-2011-TMS", "recheis-2009-arr", "REICHHOLF-2023-HRO", "Reichinger-12", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "Reimerth_Saskia_2016_RGU", "REINALTER_2010_VIS", "reinwald-2017-baa", "reisinger-2023-iad", "Reisner_2013_RSB", "Reisner_Irene_2013_R3D", "reitsamer-2008-tsud", "reznicek-2020-fpgaray", "richter-2020-ani", "riegler_2020_framework", "roch-2007-rtt", "Roch2006", "Roegner_RHF_2010", "ROEGNER-2014-TUI", "ROEGNER-2015-IBR", "ROESSLER-2012-OGLES", "roth_2021_vdst", "roth_vdi", "roth-2023-vlk", "rozanics-lsf", "rubik_michael-2022-baa", "Rudolf_06_IDM", "rumpelnik_martin_2020_PRM", "rumpelnik_martin_2020_PRM", "Rumpelnik_Martin-2020-NPR", "rumpelnik-2022-vlk", "rumpler_2019_PPC", "Rumpler-2019-PPC", "RUMPLER-2021-VOGL", "rumpler-2023-dcmcm", "S05", "SABADELLO-2006-HEG", "sakai_2013", "SAKAI-2015-EAPI", "sakai-2021-eif", "sakai-2024-asa", "sakai-2025-stater", "sakai-thesis", "santos-2017-dacge", "sari2008mt", "SATZER-2008", "schaffarz_2018-pra", "scharl-master-thesis", "schartmueller_2018_baa", "schaukowitsch-2013-fls", "SCHAUKOWITSCH-2015-REMOTE", "SCHAUKOWITSCH-2015-STELL", "schedl-2012-dof", "schedl-2013-gP4", "schedl-thesis", "schefcik_juergen_2015_baa", "Scheiblauer-2006-DA", "Scheiblauer-2008-DCW", "SCHEIBLAUER-2009-IDCE", "scheiblauer-2011-cag", "scheiblauer-2011-chnt", "scheiblauer-2012-chnt", "scheiblauer-2013-wscg", "SCHEIBLAUER-2015-WFC", "scheiblauer-thesis", "scheiblauer-thesis", "scherzer_2010a", "Scherzer-2007-PCS", "SCHERZER-2008-FSR", "scherzer-2009-isvc", "SCHERZER-2012-TCM", "scherzer-eg2010", "scherzer-thesis", "scherzer2010b", "scherzer2010d", "scherzer2010e", "scherzer2011c", "scherzer2011d", "schmid_andreas_2013-prj", "schneider-sturm-2026-mia", "schober_florian_2012_aspm", "schoerkhuber_dominik-2016-baa", "scholz_2017_bac", "schrammel_sebastian_baa", "schuetz_markus-2013-pra", "SCHUETZ-2014-RRLP", "SCHUETZ-2015-HQP", "SCHUETZ-2016-POT", "schuetz-2018-PPC", "schuetz-2019-CLOD", "schuetz-2019-LCO", "SCHUETZ-2020-MPC", "schuetz-2020-PPC", "SCHUETZ-2021-DISS", "SCHUETZ-2021-PCC", "SCHUETZ-2021-PCC", "SCHUETZ-2022-PCC", "SCHUETZ-2023-LOD", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schulz-faf", "schwaerzler_2018_phd", "schwaerzler-2009-ass", "SCHWAERZLER-2012-FAS", "SCHWAERZLER-2013-FPCSS", "SCHWAERZLER-2017-SBGM", "schwarz-2024-m2m", "Schwengerer_Mathias-2022-BGL", "seczer-2025-vrs", "seidemann-bachelor-2013", "seisenbacher-cyclesafely", "Selig_2005_WRO", "Selig-2007-VegAnim", "Semmelrath-2022-IRS", "seyr-2025-lbt", "SIETZEN-2017-BBO", "silvana_2014", "sippl-2014-fss", "slajcho_levente-baa", "slavtchev-dcd", "Spechtenhauser_Florian_2013_SIU", "spelitz_stefan-2012-CDTFMR", "sperl-2013-BA", "Sperl-praktikum-2016", "spielmann-2006-ner", "spitaler-2015-pbi", "spitaler-afr-2011", "SSMW09", "stappen_SteFAS", "stappen-2019-vrs", "starnberger-cli", "stauss-2025-gfd", "steiner_2016_isad", "steiner_2016_isad", "STEINER-2006-SVC", "steiner-2013-BS", "steiner-2014-da", "steinheber-2024-oto", "steinkellner-2022-dll", "steinkellner-2024-dll", "STEINLECHNER-2019-APS", "steinschauer-2020-baa", "steinschorn-2023-p2m", "steinschorn-2023-p2m", "steinschorn-2025-par", "steinschorn-2025-parameter", "Stingl_2011_RHS", "STINGL-2009-RHS", "Stoll2020", "STREICHER-2022-DSLS", "Streicher-bachelor-2013", "STU-SR-25", "sturl_andreas-caflm", "STURN-2013-LGI", "STURN-2013-LSI", "Sturn-2013-ST", "sturn-thesis", "stutter_david-2012-kft", "subtil-2006-disp", "SUNDSTEDT-2008-ASF", "sundstedt-2013-vag", "szabo_2013_ssrr", "Tanzmeister_2011_I3D", "tatzgern-2020-sst", "Thann_2019_01", "themmer-2018-fbx", "thurner-2019-ani", "TK_2011", "tobler02_amrmg", "tobler02_mbpls", "tobler97_hsdasr", "TOMASCHITZ-2020-EP", "TOROMANOFF-2005-ASA", "TR 193-02-2019-4", "TR-186-2-02-04", "TR-186-2-05-05", "TR-186-2-06-02", "TR-186-2-07-01", "TR-186-2-07-09", "TR-186-2-08-09", "TR-186-2-12-01", "Tragust-2012-GMHPC", "Tragust-2014-master-thesis", "traxler-1997-TRA", "trenkwalder-2008-landscape", "trenkwalder-2013-ma", "trenovatz-lpd", "trumpf_stefan_2012", "Tucek_2018", "Tucek_Tom-2016-aai", "tumpach_barbara-pra", "ulschmid-2024-reo", "ulschmid-2025-apc", "ulschmid-phdthesis", "unterguggenberger-2010-cmph", "unterguggenberger-2020-fmvr", "unterguggenberger-2021-msh", "unterguggenberger-2022-vulkan", "unterguggenberger-2023-vaw", "unterguggenberger-2024-fropo", "unterguggenberger-phdthesis", "VASCO-2019-EVR", "vasiljevic-gpc", "VASILJEVS-2018-PMPL", "vis-foa", "VOELKL-2013-RTRS", "voglreiter-2023-tro", "Voglsam_2012_RTR", "Voglsam_2013_RRT", "wagner-2024-par", "wallner_2008_ltosd", "WALLNER-2010-CSSAO", "wallner-2015-InfoTheoryPlay", "wallner-2015-ModelingRoutinization", "Wang-2016-BAC", "wang-sifuentes-2018-cgbase", "weber_2008_sh", "WEBER-2012-MIC", "WEBER-2015-PRA", "WEBER-2015-PRA1", "weidlich_2009_REL", "weidlich-2009-dispersion", "weidlich-2009-thesis", "weiner-2010-eud", "weinzierl_2012_iism", "weinzierl_heigl-2017-wh14", "Wenigwieser2007", "werdermann-va", "WESSNER-2019-PCW", "WEYDEMANN-2014-PIC", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "weydemann-praktikum-2016", "wieser-2019-ani", "wiesinger_2020_odpr", "wiesinger_2020_odpr", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wiesinger-2023-gcg", "wildt-tci", "Wilkie-2001-Ori", "Wilkie-2004-AMS", "wilkie-2006-dfs", "wilkie-2009-cc", "Wimmer-1999-FWIa", "Wimmer-1999-FWIb", "Wimmer-2001-PBI", "Wimmer-2001-Poi", "Wimmer-2003-RTE", "Wimmer-2004-LSPM", "Wimmer-2005-HOQ", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2006-PSM", "WIMMER-2006-SIV", "WIMMER-2007-GAR", "WIMMER-2007-RTR", "WIMMER-2008-ART", "WIMMER-2008-CAR", "WIMMER-2008-PVR", "WIMMER-2009-ARTR", "WIMMER-2009-ARTR2", "WIMMER-2009-ARTR3", "WIMMER-2009-CSR", "WIMMER-2009-ITC", "WIMMER-2009-VCCG", "WIMMER-2014-DWNT", "WIMMER-2015-CGFC", "WIMMER-2015-CGMC", "WIMMER-2016-FUTURIS", "WIMMER-2016-HARVEST4D", "WIMMER-2019-CGSG", "WIMMER-2020-ASG", "wimmer-2022-acd", "wimmer-2023-acd", "Win25", "WINKLER-2013-AMO", "Winkler-2017", "WINKLER-2019-PDG", "winklhofer_christoph-2013-RRMR", "winklmueller-aso", "withalm_brigitte-2022-baa", "Woerister_2012_ACS", "wolffhardt-2026-prj", "wonka-2000-VisP", "Wonka-2001-Ins", "Wonka-2001-IV", "Wonka-2003-Ins", "WONKA-2006-GVS", "x-mas-2013", "xmas-2025", "yoo-chaeran-2025-pr", "zapotocky_2013_ma", "ZAU14", "zeilinger_matthias-2020-baa", "Zezulka_Matthias-2023-MatLabOptInterface", "Zezulka-2024-FTS", "Zezulka-2025-NRL", "Zezulka-2026-FGTS", "Zezulka-2026-GridThermalSim", "zhang_le-2010-IGA", "zhang-thesis", "ZL_2011_IVIG", "zotti-2001-dip", "zotti-2003-ash", "zotti-2005-lum", "Zotti-2005-vis", "zotti-2006-dgm", "zotti-2006-pla", "zotti-2007-eg", "zotti-2007-hdr", "zotti-2007-PhD", "zotti-2007-wscg", "ZOTTI-2016-VAA", "ZOTTI-2017-BM", "ZOTTI-2017-TSP", "zsolnai-2018-gms", "zsolnai-feher-thesis-2019", "zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2011/ohrhallinger_stefan-2011-001/", "__class": "Publication" } ]