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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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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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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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"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" } ], "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": "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", 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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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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 the case of autonomous driving, a\n\ncombination of modalities is used to complement other modalities weaknesses.\nWhile LIDAR sensors offer great depth and reflection information precision, the\noutput is a sparsely populated point cloud. RGB images, on the other hand,\noffer great resolution and improve object detection and classification results.\nHowever, to be able to extract precise 3D bounding cuboids, it is often fusioned\nwith point clouds or other depth information.", "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": 468, "name": "mueller-2024-rbt-image.png", "type": "image/png", "size": 493687, "path": "Publication:mueller-2024-rbt", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/mueller-2024-rbt/mueller-2024-rbt-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/mueller-2024-rbt/mueller-2024-rbt-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5355, "drupal_id": null, "orcid_id": null, 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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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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.

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Kameraparameter, das muss transformiert werden, sodass es sich überdeckt)\n- Change Detection (implementiert wer anderer) durchführen, die Punkte markiert: entweder infinitam.org auf Android portieren oder mittels Client/Server an den Server an der TU falls nicht möglich\n- Changes als markierte Pixel (Augmented Reality) über dem aktuellen RGB-Bild auf dem Smartphone anzeigen\n- Evaluierung: wie schnell kann die Registrierung, Change Detection und Visualisierung dargestellt werden und wie genau (ausgenommen Change detection) ist sie\n\n\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 489, "image_height": 254, "name": "hamzic_ademir-baa-image.png", "type": "image/png", "size": 162798, "path": "Publication:hamzic_ademir-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/hamzic_ademir-baa/hamzic_ademir-baa-image.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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}, { "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" } ], "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" } ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": true, "use_in_gallery": false, "access": "public", "name": "kozonits_marcel-2025-baa-.pdf", "type": "application/pdf", "size": 902236, "path": "Publication:kozonits_marcel-2025-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kozonits_marcel-2025-baa/kozonits_marcel-2025-baa-.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/kozonits_marcel-2025-baa/kozonits_marcel-2025-baa-:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 209, "image_height": 135, "name": "kozonits_marcel-2025-baa-image.png", "type": "image/png", "size": 45093, "path": "Publication:kozonits_marcel-2025-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kozonits_marcel-2025-baa/kozonits_marcel-2025-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/kozonits_marcel-2025-baa/kozonits_marcel-2025-baa-image: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/2025/kozonits_marcel-2025-baa/", "__class": "Publication" }, { "id": "wiesinger_2025-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Using a drone for automated 3D Scanning", "date": "2025-03-28", "abstract": "Drones have been widely adopted over the past decades, aiding in critical tasks such as\nsearch and rescue, inspection, and mapping. Autonomous drone scanning could further\nsupport such missions, but remains a significant challenge in indoor environments. This\nthesis explores the feasibility of using a consumer drone for such autonomous indoor 3D\nscanning. Our approach combines a DJI Spark drone, a lightweight Pico Flexx depth\nsensor, and a Raspberry Pi to capture depth data, which is streamed to an external\nserver for real-time processing. The system leverages ROS 2 and InfiniTAM for SLAM\nand map reconstruction, while navigation commands are issued via a smartphone using\nDJI’s Mobile SDK.\nAlthough the system successfully completed a limited autonomous scan, various con-\nstraints—including the drone’s payload capacity, limited sensor range, and hardware\ninstabilities—posed significant challenges. Despite these limitations, a modular software\narchitecture was developed that integrates sensing, mapping, and navigation. This\nframework provides a solid foundation for future work toward fully autonomous indoor\nscanning with more capable hardware. However, generating the next best view and\nfinding a feasible path toward it remain open challenges.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5296, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Klemens", "lastname": "Wiesinger", "searchname": "Klemens Wiesinger", "is_teacher": false, "name": "Klemens Wiesinger", "publications": [ "wiesinger_2025-baa", "wiesinger_klemens-2023-baa" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5296", "__class": "Author" } ], "date_end": "2025-03-28", "date_start": "2023-07-13", "matrikelnr": "11938253", "supervisor": [ { "id": 193, "drupal_id": 49, "orcid_id": "0000-0002-9370-2663", "website": "https://www.cg.tuwien.ac.at/staff/MichaelWimmer", "firstname": "Michael", "lastname": "Wimmer", "searchname": "Michael Wimmer", "is_teacher": true, "name": "Michael Wimmer", "publications": [ "arikan-2013-osn", "arikan-2014-pcvis", "arikan-2015-dmrt", "Arleo-2019-vis", "Artusi-2003-Del", "Auzinger_2012_GeigerCam", "Auzinger_2013_AnaVis", "Auzinger_2013_NSAA", "Auzinger_2013_SAR", "Auzinger_Mistelbauer_2013_CSR", "bagar2010", "bernhard-2010-gph", "bernhard-2011-bmtf", "bernhard-2011-maicg", "bernhard-2014-EFD", "bernhard-2014-GTOM", "birsak-2013-sta", "birsak-2014-agtb", "birsak-2017-dpe", "Bittner-2001-Vis", "Bittner-2004-CHC", "bittner-2005-egsr", "BITTNER-2009-AGVS", "BITTNER-2009-GEFOC", "bittner-2011-scc", "BOKSANSKY-2019-RTS", "brugger-2020-tdp", "CADIK-2006-IAQ", "CADIK-2008-EHD", "cardoso-2021-cost", "cardoso-2022-rtpercept", "cardoso-2024-r-c", "celarek_adam-2019-qelta", "celarek-2022-gmcn", "celarek-2025-d3g", "CHARALAMBOS-2007-HLOD", "CORNEL-2017-FRS", "CORNEL-2019-IVF", "dataset-viennacity", "ecormier-nocca-2025-sls", "eibens-2021-tsgpmg", "EISEMANN-2011-RTS", "EISEMANN-2012-ERT", 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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.

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"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" } ], "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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"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", 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be image of plant bed (with several diverse plants), pipeline with segmentation (in same paper, or find segmentation paper)\n- optional: classify and segment plants\n- optional: growth analysis: blueten, wie schnell waechst die pflanze\n- search for data sets for training for generic plants (as many plants as possible), possibly combine several\n- evaluation with cheap IP RGB camera, and multispectral camera, e.g., RGB+IR or more\n- optional: evaluate soil dryness from images with dryness sensor\n \nresearch question: how well does such a system work with a commodity setup - for indoor and outdoor\nablation studies for indoor (more controlled environment) and outdoor, as well as RGB or RGB+IR++", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 332, "image_height": 332, "name": "steinheber-2024-oto-image.png", "type": "image/png", "size": 212356, "path": 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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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"path": "Publication:steinheber-2024-oto", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/steinheber-2024-oto/steinheber-2024-oto-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/steinheber-2024-oto/steinheber-2024-oto-image: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/2025/steinheber-2024-oto/", "__class": "Publication" }, { "id": "pointner_simon-2022-maa", "type_id": "masterthesis", "tu_id": null, "repositum_id": "20.500.12708/225169", "title": "Cycle Safely", "date": "2025", "abstract": "This thesis presents a design and implementation of an end-to-end collision prediction and detection pipeline tailored to cyclists. The primary goal is to predict potential collisions between the cyclist (ego agent) and other road users, particularly motorised vehicles, which pose a higher risk due to higher momentum and speed. The proposed pipeline integrates conventional techniques for solving the sub-tasks of object detection, object tracking, and trajectory forecasting. Specifically, Super Fast and Accurate 3D Object Detection (SFA3D) is used for the detection, a Kalman filter-based multi-object tracker for temporal association of these detections, and a machine learning-based model (prediction conditioned on goals in visual multi-agent settings) is adapted and trained for the prediction of future trajectories. CARLA driving simulator facilitates the training and development of the prediction model by creating a synthetic dataset of cycling and the interaction with other road users. The system is evaluated on the synthetic dataset and also on the real-world KITTI dataset, and additional ablation studies examine the contribution of each pipeline stage. Experiments demonstrate that the proposed approach is capable of achieving reliable performance in object detection and tracking tasks. This confirms the feasibility of such a pipeline under limited sensing capabilities, such as LIDAR and GPS/IMU measurements. However, trajectory prediction remains a difficult and computationally expensive task, primarily due to the lack of documented and easily deployable open-source models. The implementation comes with a visualisation framework, built from the Rerun tool, for interactive inspection of the pipeline’s intermediate and final results.The contribution of this thesis can be summarised by the implementation of a framework for cyclist collision detection. It offers insights into how conventional and machine learning methods can be combined into a pipeline, and key limitations and points of future work for the creation of better trajectory prediction in adverse traffic contexts are outlined.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1796, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Simon", "lastname": "Pointner", "searchname": "Simon Pointner", "is_teacher": false, "name": "Simon Pointner", "publications": [ "pointner_simon-2020", "pointner_simon-2022-maa" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1796", "__class": "Author" } ], "ac_number": "AC17754544", "co_supervisor": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "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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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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"https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-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/2024/marin-2024-sing/", "__class": "Publication" }, { "id": "hoffer-toth-com", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "CycleSafely on mobile", "date": "2024-08-12", "abstract": "SFA3D:\n\nZeitplan: Auf Mobile portiert bis Ende März / Anfang April.\n\nSAF3D ist mit Pytorch implementiert worden. Folgende Möglichkeiten gibt es um Pytorch Modelle auf Mobile/Android zu portieren:\n\n- Pytorch Mobile: https://pytorch.org/mobile/home/\n\n- Pytorch Flutter Plugins: https://pub.dev/packages/flutter_pytorch oder https://pub.dev/packages/pytorch_mobile\n\n- Deep Java Library: https://djl.ai/\n\n \n\n3D-Multi-Object-Tracker:\n\nZeitplan: Auf Mobile implementiert bis Ende April.\n\nDa 3DMOT ein non-ml Algorithmus ist, kann er in der dann gewählten Sprache implementiert werden.\n\n \n\nPrecog:\n\nZeitplan: Auf Mobile portiert bis Ende Mai.\n\nPrecog habe ich leider nicht zum Laufen bekommen, da ich nicht alle Daten gefunden habe bzw. nicht sicher war welche benötigt werden und wie sie konfiguriert werden sollen.\n\nPrecog verwendet Tensorflow. Dazu habe ich folgendes gefunden:\n\n- Tensorflow Lite: https://www.tensorflow.org/lite\n\n- Tflite Flutter: https://pub.dev/packages/tflite_flutter\n\n \n\nVergleich zwischen Server und Mobile:\n\nZeitplan: Server und Mobile werden Anfang Juni verglichen, wenn bis dahin alles nach Plan läuft.\n\n \n\nSchriftliche Arbeit:\n\nZeitplan: Mit dem Schreiben wird nach dem Vergleichen angefangen und dafür kann der Rest von Juni verwendet werden. Falls sich eines der Schritte herauszögert, dann kann ich die vorlesungsfreie Zeit im Sommer auch verwenden.\n\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 614, "image_height": 613, "name": "hoffer-toth-com-image.png", "type": "image/png", "size": 164978, "path": "Publication:hoffer-toth-com", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/hoffer-toth-com/hoffer-toth-com-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/hoffer-toth-com/hoffer-toth-com-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5353, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Matyas", "lastname": "Hoffer-Toth", "searchname": "Matyas Hoffer-Toth", "is_teacher": false, "name": "Matyas Hoffer-Toth", "publications": [ "hoffer-toth-com" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5353", "__class": "Author" } ], "date_end": "2024-08-12", "date_start": "2024-03-11", "matrikelnr": "12122086", "supervisor": [ { "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": 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"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" } ], "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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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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"research_areas": [], "keywords": [ "CCS Concepts", "Graph algorithms", "Mesh geometry models", "Paths and connectivity problems" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "name": "marin-2024-rcf-image.png", "type": "image/png", "size": 706684, "path": "Publication:marin-2024-rcf", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-rcf/marin-2024-rcf-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-rcf/marin-2024-rcf-image: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/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", 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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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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. 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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": "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/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/show.php?class=Author&id=823", "__class": "Author" }, { "id": 5297, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Pooran", "lastname": "Memari", "searchname": "Pooran Memari", "is_teacher": false, "name": "Pooran Memari", "publications": [ "marin-2024-sing", "ohrhallinger_stefan-2023-con", "parakkat-2024-ballmerge" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5297", "__class": "Author" } ], "doi": "10.1111/cgf.15019", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "2", "open_access": "yes", "pages": "13", "publisher": "WILEY", "volume": "43", "research_areas": [], "keywords": [ "surface reconstruction", "voronoi", "point clouds" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 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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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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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guided scanning infinitam plugin to drone\n- evaluate with manual control with a user study on how fast and complete an interior room can be scanned", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 480, "image_height": 313, "name": "wiesinger_klemens-2023-baa-image.png", "type": "image/png", "size": 233669, "path": "Publication:wiesinger_klemens-2023-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/wiesinger_klemens-2023-baa/wiesinger_klemens-2023-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/wiesinger_klemens-2023-baa/wiesinger_klemens-2023-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5296, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Klemens", 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"path": "Publication:wiesinger_klemens-2023-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/wiesinger_klemens-2023-baa/wiesinger_klemens-2023-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/wiesinger_klemens-2023-baa/wiesinger_klemens-2023-baa-image: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.

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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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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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"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/rubik_michael-2022-baa/", "__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", "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" }, { "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", 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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": [ "Curve reconstruction", "Spheres-of-influence graph" ], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.1111/cgf14654", "caption": "Paper", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": false, "access": "public", 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"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/2022/marin-2022-sigdt/", "__class": "Publication" }, { "id": "elagrod_ahmed-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Immersive Redesign", "date": "2022-09-05", "abstract": "With scanning technologies and the processing power of computers increasing it is getting easier\nto scan the surrounding area and upload a 3D model of it. Nowadays mobile phones and drones\nare capable of doing that. However, the results most likely are not as good as dedicated scanners.\nTo offer people an option to explore their scanned objects my colleague Manuel Keilman and\nI wrote a software with which users can upload multiple point clouds. Those point clouds can\nthen be modified by adding, deleting, or moving parts of it. Our primary target is point clouds\nof street scenes. We selected an existing classification algorithm called GraND-Net [13] to\nidentify objects of the scene. Since the classification algorithm does not cluster the classes a\nseparate algorithm for that was needed. That is why we use DBScan [15] in order to distinguish\ndifferent objects of the same class. Both steps are done before starting the application since they\nonly have to be done once. The benefit of grouping points with similar features is that users\ndo not have to manually select everything which improves the usability of our software. After\nclassifying as well as clustering users can upload their point clouds and then select objects in\norder to modify them. Mistakes tend to happen which is why our software also offers users the\noption to manually classify whatever they want. All of that should make it easier for users to\nanalyze and modify their 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": 556, "image_height": 213, "name": "elagrod_ahmed-baa-image.png", "type": "image/png", "size": 225625, "path": "Publication:elagrod_ahmed-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/elagrod_ahmed-baa/elagrod_ahmed-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/elagrod_ahmed-baa/elagrod_ahmed-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5287, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Ahmed", "lastname": "El Agrod", "searchname": "Ahmed El Agrod", "is_teacher": false, "name": "Ahmed El Agrod", "publications": 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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. A survey paper compares this body of work with all related work in this now mature field and includes an open source benchmark that allows to easily evaluate competing algorithms in multiple aspects and highlights their relative strengths. For selected 2D algorithms, extensions to 3D are given, as well as offering many novel perspectives for 3D reconstruction, where important open problems remain. As a different topic, when visualizing point clouds, occlusion can be inferred for almost free by exploiting the fact that point clouds representing surfaces are inherently 2D and squashing them in a view-based 2D data structure. 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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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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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600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/marin-2022-sig-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "poster", "main_file": true, "use_in_gallery": true, "access": "public", "preview_image_width": 6623, "preview_image_height": 9363, "name": "marin-2022-sig-poster.pdf", "type": "application/pdf", "size": 1622814, "path": "Publication:marin-2022-sig", "preview_name": "marin-2022-sig-poster:preview.png", "preview_type": "image/png", "preview_size": 2085570, "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/marin-2022-sig-poster.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/marin-2022-sig-poster: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/2022/marin-2022-sig/", "__class": "Publication" }, { "id": "steinschauer-2020-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Change Detection using the InfiniTAM framework", "date": "2022-01-24", "abstract": "The automated detection of changes in a 3D space can be a useful tool. [PCBS16] names\n3D surface reconstruction, environment monitoring, natural events management, and\nforensic science as possible application scenarios. In this work, we introduce software\nthat scans an area at two different points in time and detects the changes between these\nscans. The software is based on InfiniTAM [PKG+17], a framework released under an\nOxford University License. InfiniTAM integrates multiple depth images (e.g. recorded\nwith a Kinect-V2-Camera) to a 3D model using volumetric representations. Because of\nthe volumetric representation and the fast GPU computation, the change detection can\nhappen in real-time. This is outstanding because in other approaches, (like [PCBS16])\nthe change detection can take minutes. Other approaches that detect changes in real-time\n(like [KMK+19]) use the same representation of data (T-SDF) as we do. Our approach\nalso takes sensor tolerance into account, which leads to a reduction of false change\ndetections. This work can be seen as a starting point for more specific use cases (like\n[LTW+21]) who specify on scene change detection and overcoming unnecessary changes\nsuch as light and seasons.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1065, "image_height": 628, "name": "steinschauer-2020-baa-image.png", "type": "image/png", "size": 679657, "path": "Publication:steinschauer-2020-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/steinschauer-2020-baa/steinschauer-2020-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/steinschauer-2020-baa/steinschauer-2020-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1755, "drupal_id": null, "orcid_id": null, "website": null, "firstname": 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"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/2022/steinschauer-2020-baa/", "__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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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 on 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. 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"https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-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/2021/ohrhallinger-2021-egs/", "__class": "Publication" }, { "id": "depner_dennis-2020-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "3D Scan Integration", "date": "2021-03-01", "abstract": "3D scanning is often not complete after a single pass from a single sensor. Multiple scanners, e.g. from a crowd, or multiple autonomous vehicles, may contribute data simultaneously. Or, after looking at the resulting model, more passes may be made to fill holes or improve the quality.\n\nThis requires updating a 3D reconstruction with new points, integrating those into the model and considering them equally with the existing points. In order to avoid dynamic and massive storage requirements, their coordinate information required for reconstruction can be stored as single median+variance vectors, which can be updated incrementally with new points, see e.g.: http://datagenetics.com/blog/november22017/index.html). With the local information at nodes, marching cubes can be used to generate a triangulation at grid cells. Since the octree has varying depths at leaf nodes, we need to apply an adapted version from an existing algorithm, Screened Poisson (for source and paper see: http://www.cs.jhu.edu/~misha/Code/PoissonRecon/Version8.0/). See also http://infinitam.org for the source code and the paper it is based on.\n\nTasks:\n Use a Kinect 3D scanner with the Infinitam software to scan several overlapping passes of an interior room, resulting in an octree with data in its nodes\n Hand-align scans with Meshlab or register them using ICP (http://pointclouds.org/documentation/tutorials/iterative_closest_point.php) so that they correspond spatially\n Add new points into octree nodes which overlap in space\n Apply a provided surface orientation operator which uses median+variance of nodes in order to mark vertices of nodes as in- or outside\n Create a mesh from the octree on demand for visualization, using marching cubes adapted to octrees as in Screened Poisson\n Evaluate the quality of the incrementally created reconstruction with a single-pass reconstruction of a merged point cloud where all points are considered at once\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": 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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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