[ { "id": "marin-thesis", "type_id": "phdthesis", "tu_id": null, "repositum_id": "20.500.12708/215385", "title": "Proximity-Based Point Cloud Reconstruction", "date": "2025-02", "abstract": "Extrapolating information from incomplete data is a key human skill, enabling us to inferpatterns and make predictions from limited observations. A prime example is our ability to perceive coherent shapes from seemingly random point sets, a key aspect of cognition.However, data reconstruction becomes challenging when no predefined rules exist, as it is unclear how to connect the data or infer patterns. In computer graphics, a major goal isto replicate this human ability by developing algorithms that can accurately reconstruct original structures or extract meaningful information from raw, disconnected data.The contributions of this thesis deal with point cloud reconstruction, leveraging proximity-based methods, with a particular focus on a specific proximity-encoding data structure -the spheres-of-influence graph (SIG). We discuss curve reconstruction, where we automate the game of connecting the dots to create contours, providing theoretical guarantees for our method. We obtain the best results compared to similar methods for manifold curves. We extend our curve reconstruction to manifolds, overcoming the challenges of moving to different domains, and extending our theoreticalguarantees. We are able to reconstruct curves from sparser inputs compared to the state-of-the-art, and we explorevarious settings in which these curves can live. We investigate the properties of the SIGas a parameter-free proximity encoding structure of three-dimensional point clouds. We introduce new spatial bounds for the SIG neighbors as a theoretical contribution. We analyze how close the encoding is to the ground truth surface compared to the commonly used kNN graphs, and we evaluate our performance in the context of normal estimationas an application. Lastly, we introduce SING – a stability-incorporated neighborhood graph, a useful tool with various applications, such as clustering, and with a strong theoretical background in topological data analysis.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2012, "image_height": 1035, "name": "marin-thesis-Teaser.png", "type": "image/png", "size": 599974, "path": "Publication:marin-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Teaser:thumb{{size}}.png" }, "sync_repositum_override": "access,date", "repositum_presentation_id": null, "authors": [ 1848 ], "ac_number": "AC17518474", "co_supervisor": [ 948 ], "date_end": "2025-02", "date_start": "2021-09", "doi": "10.34726/hss.2025.128668", "duration": "3 years", "open_access": "yes", "pages": "141", "reviewer_1": [ 1510 ], "reviewer_2": [ 1243 ], "rigorosum": "2025-02-13", "supervisor": [ 193 ], "research_areas": [], "keywords": [ "point clouds", "reconstruction", "proximity graphs", "curve reconstruction", "clustering", "geometry processing" ], "weblinks": [], "files": [ { "description": null, "filetitle": "Curve Examples", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2080, "image_height": 653, "name": "marin-thesis-Curve Examples.png", "type": "image/png", "size": 170210, "path": "Publication:marin-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Curve Examples.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Curve Examples:thumb{{size}}.png" }, { "description": null, "filetitle": "Possible Reconstructions", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2078, "image_height": 678, "name": "marin-thesis-Possible Reconstructions.png", "type": "image/png", "size": 118537, "path": "Publication:marin-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Possible Reconstructions.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Possible Reconstructions:thumb{{size}}.png" }, { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2012, "image_height": 1035, "name": "marin-thesis-Teaser.png", "type": "image/png", "size": 599974, "path": "Publication:marin-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-thesis-thesis.pdf", "type": "application/pdf", "size": 39191621, "path": "Publication:marin-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "rend" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/", "__class": "Publication" }, { "id": "marin-2024-sing", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/208566", "title": "SING: Stability-Incorporated Neighborhood Graph", "date": "2024-12", "abstract": "We introduce the Stability-Incorporated Neighborhood Graph (SING), a novel density-aware structure designed to capture the intrinsic geometric properties of a point set. We improve upon the spheres-of-influence graph by incorporating additional features to offer more flexibility and control in encoding proximity information and capturing local density variations. Through persistence analysis on our proximity graph, we propose a new clustering technique and explore additional variants incorporating extra features for the proximity criterion. Alongside the detailed analysis and comparison to evaluate its performance on various datasets, our experiments demonstrate that the proposed method can effectively extract meaningful clusters from diverse datasets with variations in density and correlation. Our application scenarios underscore the advantages of the proposed graph over classical neighborhood graphs, particularly in terms of parameter tuning.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2607, "image_height": 709, "name": "marin-2024-sing-image.png", "type": "image/png", "size": 897420, "path": "Publication:marin-2024-sing", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1848, 1825, 948, 193, 5431, 5297 ], "booktitle": "SA '24: SIGGRAPH Asia 2024 Conference Papers", "date_from": "2024-12-03", "date_to": "2024-12-06", "doi": "10.1145/3680528.3687674", "editor": "Igarashi, Takeo and Shamir, Ariel and Zhang, Hao", "event": "SA '24: SIGGRAPH Asia 2024", "isbn": "979-8-4007-1131-2", "lecturer": [ 1848 ], "location": "Tokyo", "pages": "10", "pages_from": "1", "pages_to": "10", "publisher": "Association for Computing Machinery", "research_areas": [ "Geometry" ], "keywords": [ "Proximity graphs", "Stipple art editing", "Pattern design", "Network topology", "clustering", "point patterns", "similarity metric", "discrete distributions", "persistence analysis", "Neighborhood graph", "topological data analysis", "K-means", "Rips complexes" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2607, "image_height": 709, "name": "marin-2024-sing-image.png", "type": "image/png", "size": 897420, "path": "Publication:marin-2024-sing", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-2024-sing-paper.pdf", "type": "application/pdf", "size": 12994577, "path": "Publication:marin-2024-sing", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/", "__class": "Publication" }, { "id": "marin-2024-rcf", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/202374", "title": "Reconstructing Curves from Sparse Samples on Riemannian Manifolds", "date": "2024-06", "abstract": "Reconstructing 2D curves from sample points has long been a critical challenge in computer graphics, finding essential applications in vector graphics. The design and editing of curves on surfaces has only recently begun to receive attention, primarily relying on human assistance, and where not, limited by very strict sampling conditions. In this work, we formally improve on the state-of-the-art requirements and introduce an innovative algorithm capable of reconstructing closed curves directly on surfaces from a given sparse set of sample points. We extend and adapt a state-of-the-art planar curve reconstruction method to the realm of surfaces while dealing with the challenges arising from working on non-Euclidean domains. We demonstrate the robustness of our method by reconstructing multiple curves on various surface meshes. We explore novel potential applications of our approach, allowing for automated reconstruction of curves on Riemannian manifolds.", "authors_et_al": false, "substitute": null, "main_image": { "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" }, "sync_repositum_override": "date_from,date_to,event,lecturer,location,pages_from,pages_to", "repositum_presentation_id": "20.500.12708/202064", "authors": [ 1848, 5388, 5389, 948, 193 ], "articleno": "e15136", "date_from": "2024-06-24", "date_to": "2024-06-26", "doi": "10.1111/cgf.15136", "event": "Symposium on Geometry Processing", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1848 ], "location": "Boston", "number": "5", "pages": "14", "pages_from": "-", "pages_to": "-", "publisher": "WILEY", "volume": "43", "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": [ "WorldScale" ], "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": [ 948, 1848 ], "event": "TU Graz", "location": "Graz", "research_areas": [ "Geometry" ], "keywords": [ "curve reconstruction", "surface reconstruction", "sampling" ], "weblinks": [], "files": [ { "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" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ohrhallinger_stefan-2024-inv/", "__class": "Publication" }, { "id": "marin-2024-dsr", "type_id": "poster", "tu_id": null, "repositum_id": "20.500.12708/201670", "title": "Distributed Surface Reconstruction", "date": "2024-04", "abstract": "Recent advancements in scanning technologies and their rise in availability have shifted the focus from reconstructing surfaces from point clouds of small areas to large, e.g., city-wide scenes, containing massive amounts of data. We adapt a surface reconstruction method to work in a distributed fashion on a high-performance cluster, reconstructing datasets with millions of vertices in seconds by exploiting the locality of the connectivity required by the reconstruction algorithm to efficiently divide-and-conquer the problem of creating triangulations from very large unstructured point clouds.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2017, "image_height": 1227, "name": "marin-2024-dsr-image.png", "type": "image/png", "size": 3376780, "path": "Publication:marin-2024-dsr", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1848, 1859, 948, 193 ], "booktitle": "EG 2024 - Posters", "cfp": { "name": "Call for Posters _ EUROGRAPHICS.pdf", "type": "application/pdf", "error": 0, "size": 210661, "orig_name": "Call for Posters _ EUROGRAPHICS.pdf", "ext": "pdf" }, "date_from": "2024-04-22", "date_to": "2024-04-26", "doi": "10.2312/egp.20241037", "editor": "Liu, Lingjie and Averkiou, Melinos", "event": "45th Annual Conference of the European Association for Computer Graphics (Eurographics 2024)", "isbn": "978-3-03868-239-4", "lecturer": [ 1848 ], "location": "Limassol", "pages": "2", "research_areas": [], "keywords": [ "Surface Reconstruction", "Distributed Computing", "Point Clouds", "High Performance Computing", "delaunay triangulation" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2017, "image_height": 1227, "name": "marin-2024-dsr-image.png", "type": "image/png", "size": 3376780, "path": "Publication:marin-2024-dsr", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "marin-2024-dsr-paper.pdf", "type": "application/pdf", "size": 5699964, "path": "Publication:marin-2024-dsr", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/marin-2024-dsr-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/", "__class": "Publication" }, { "id": "ehlers-2024-vgs", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/196069", "title": "Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities", "date": "2024-03-11", "abstract": "Compound graphs are common across domains, from social science to biochemical pathway studies, and their visualization is important to both their exploration and analysis. However, effectively visualizing a compound graph's topology and group structure requires careful consideration, as evident by the many different approaches to this particular problem. To better understand the current advancements in compound graph visualization, we have consolidated and streamlined existing surveys' taxonomies. More specifically, we aim to disentangle the visual relationship between graph topology and group structure from the visual encoding used to visualize its group structure in order to identify interesting gaps in the literature. In so doing, we are able to enumerate a number of lessons learned and gain a better understanding of the outstanding research opportunities and practical implications across domains.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 1848, 1464, 1410 ], "booktitle": "Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1, HUCAPP and IVAPP", "date_from": "2024-02-27", "date_to": "2024-02-29", "doi": "10.5220/0012431200003660", "event": "19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications", "isbn": "978-989-758-679-8", "lecturer": [ 1850 ], "location": "Rom", "pages": "12", "pages_from": "697", "pages_to": "708", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "compound graph visualization", "literature survey", "group structure visualization" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-vgs/", "__class": "Publication" }, { "id": "marin-2024-pcp", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/196496", "title": "Parameter-free connectivity for point clouds", "date": "2024-02", "abstract": "Determining connectivity in unstructured point clouds is a long-standing problem that has still not been addressed satisfactorily. In this paper, we analyze an alternative to the often-used k-nearest neighborhood (kNN) graph - the Spheres of Influence Graph (SIG). We show that the edges that are neighboring each vertex are spatially bounded, which allows for fast computation of SIG. Our approach shows a better encoding of the ground truth connectivity compared to the kNN for a wide range of k, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., parameter-free normal estimation, and consequently, surface reconstruction, motion planning, simulations, and many more.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 742, "image_height": 912, "name": "marin-2024-pcp-image.jpg", "type": "image/jpeg", "size": 190057, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1848, 948, 193 ], "booktitle": "Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1, HUCAPP and IVAPP", "date_from": "2024-02-27", "date_to": "2024-02-29", "doi": "10.5220/0012394900003660", "event": "19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2024)", "isbn": "978-989-758-679-8", "lecturer": [ 1848 ], "location": "Rome", "open_access": "yes", "pages": "11", "pages_from": "92", "pages_to": "102", "publisher": "SciTePress, Science and Technology Publications", "volume": "1", "research_areas": [ "Geometry" ], "keywords": [ "Proximity Graphs", "Point Clouds", "Connectivity" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 742, "image_height": 912, "name": "marin-2024-pcp-image.jpg", "type": "image/jpeg", "size": 190057, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-2024-pcp-paper.pdf", "type": "application/pdf", "size": 2193223, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "Superhumans", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/", "__class": "Publication" }, { "id": "marin-2023-pic", "type_id": "poster", "tu_id": null, "repositum_id": "20.500.12708/193751", "title": "Parameter-Free and Improved Connectivity for Point Clouds", "date": "2023-05-03", "abstract": "Determining connectivity in unstructured point clouds is a long-standing problem that is still not addressed satisfactorily. 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": [ 1848, 948, 193 ], "booktitle": "Eurographics 2023 - Posters", "cfp": { "name": "Screenshot from 2024-02-20 17-30-41.png", "type": "image/png", "error": 0, "size": 212121, "orig_name": "Screenshot from 2024-02-20 17-30-41.png", "ext": "png" }, "date_from": "2023-05-08", "date_to": "2023-05-12", "doi": "10.2312/egp.20231023", "editor": "Singh, Gurprit and Chu, Mengyu", "event": "Eurographics 2023", "isbn": "978-3-03868-211-0", "lecturer": [ 1848 ], "location": "Saarbrücken", "open_access": "yes", "pages": "2", "pages_from": "5", "pages_to": "6", "publisher": "Eurographics", "research_areas": [ "Geometry" ], "keywords": [ "Computing methodologies", "Point based models" ], "weblinks": [], "files": { "1": { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-2023-pic-paper.pdf", "type": "application/pdf", "size": 518325, "path": "Publication:marin-2023-pic", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-paper:thumb{{size}}.png" } }, "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/", "__class": "Publication" }, { "id": "marin-2022-sigdt", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/175713", "title": " SIGDT: 2D Curve Reconstruction", "date": "2022-10-05", "abstract": "Determining connectivity between points and reconstructing their shape boundaries are long-standing problems in computer graphics. One possible approach to solve these problems is to use a proximity graph. We propose a new proximity graph computed by intersecting the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG) with the Delaunay triangulation (DT). We prove that the resulting graph, which we name SIGDT, contains the piece-wise linear reconstruction for a set of unstructured points in the plane for a sampling condition superseding current bounds and capturing well practical point sets' properties. As an application, we apply a dual of boundary adjustment steps from the CONNECT2D algorithm to remove the redundant edges. We show that the resulting algorithm SIG-CONNECT2D yields the best reconstruction accuracy compared to state-of-the-art algorithms from a recent comprehensive benchmark, and the method offers the potential for further improvements, e.g., for surface reconstruction.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 7066, "image_height": 3125, "name": "marin-2022-sigdt-Teaser.png", "type": "image/png", "size": 457323, "path": "Publication:marin-2022-sigdt", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Teaser:thumb{{size}}.png" }, "sync_repositum_override": "issn", "repositum_presentation_id": null, "authors": [ 1848, 948, 193 ], "booktitle": "Pacific Graphics 2022", "cfp": { "name": "Pacific Graphics 2022 - Call for papers.pdf", "type": "application/pdf", "error": "0", "size": "30666", "orig_name": "Pacific Graphics 2022 - Call for papers.pdf", "ext": "pdf" }, "date_from": "2022-10-05", "date_to": "2022-10-08", "doi": "10.1111/cgf.14654", "event": "30th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2022", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1848 ], "location": "Kyoto, Japan", "number": "7", "open_access": "yes", "pages": "12", "pages_from": "25", "pages_to": "36", "publisher": "The Eurographics Association and John Wiley & Sons Ltd.", "volume": "41", "research_areas": [ "Geometry" ], "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", "name": "marin-2022-sigdt-Paper.pdf", "type": "application/pdf", "size": 2877751, "path": "Publication:marin-2022-sigdt", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Paper.pdf", "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-Paper:thumb{{size}}.png" }, { "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" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/", "__class": "Publication" }, { "id": "marin-2022-sig", "type_id": "poster", "tu_id": null, "repositum_id": "20.500.12708/175985", "title": "SIG-based Curve Reconstruction", "date": "2022-04-25", "abstract": "We introduce a new method to compute the shape of an unstructured set of two-dimensional points. The algorithm exploits the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG). We filter edges from the Delaunay triangulation belonging to the SIG as an initial graph and apply some additional processing plus elements from the Connect2D algorithm. This combination already shows improvements in curve reconstruction, yielding the best reconstruction accuracy compared to state-of-the-art algorithms from a recent comprehensive benchmark, and offers potential of further improvements.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1822, "image_height": 1121, "name": "marin-2022-sig-image.png", "type": "image/png", "size": 112024, "path": "Publication:marin-2022-sig", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/marin-2022-sig-image.png", "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-image:thumb{{size}}.png" }, "sync_repositum_override": "projects,date,location,issn", "repositum_presentation_id": null, "authors": [ 1848, 948, 193 ], "booktitle": "Eurographics 2022 - Posters", "cfp": { "name": "029-030.pdf", "type": "application/pdf", "error": "0", "size": "189336", "orig_name": "029-030.pdf", "ext": "pdf" }, "date_from": "2022-04-25", "date_to": "2022-04-29", "doi": "10.2312/egp.20221013", "editor": "Sauvage, Basile and Hasic-Telalovic, Jasminka", "event": "Eurographics 2022 - 43rd Annual Conference of the European Association for Computer Graphics", "isbn": "978-3-03868-171-7", "issn": "1017-4656", "lecturer": [ 1848 ], "location": "Reims, France", "open_access": "yes", "pages": "2", "publisher": "The Eurographics Association", "research_areas": [ "Geometry" ], "keywords": [ "Curve reconstruction", "Spheres-of-influence graph", "Computing methodologies", "Point-based models" ], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.2312/egp20221013", "caption": "Paper", "description": null, "main_file": 1 }, { "href": "https://www.youtube.com/watch?v=JExKRTSI0Po", "caption": "Video Presentation", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1822, "image_height": 1121, "name": "marin-2022-sig-image.png", "type": "image/png", "size": 112024, "path": "Publication:marin-2022-sig", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/marin-2022-sig-image.png", "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-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-2022-sig-paper.pdf", "type": "application/pdf", "size": 189336, "path": "Publication:marin-2022-sig", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/marin-2022-sig-paper.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-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": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sig/", "__class": "Publication" } ]