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175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/proelss-bdp/proelss-bdp-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "proelss-bdp-thesis.pdf", "type": "application/pdf", "size": 879923, "path": "Publication:proelss-bdp", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/proelss-bdp/proelss-bdp-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/proelss-bdp/proelss-bdp-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/proelss-bdp/", "__class": "Publication" }, { "id": "hamzic_ademir-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Visualizing change detection on mobile", "date": "2025-08-12", "abstract": "Tasks:\n- Mit ToF Kamera und RGB Kamera (neueres Android Smartphone, Kinect, OAK-D Wide Pro) Tiefenbild und RGB Bild aufnehmen (haben jeweils leicht verschiedene 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", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/hamzic_ademir-baa/hamzic_ademir-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5351 ], "date_end": "2025-08-12", "date_start": "2024-03-15", "matrikelnr": "11937905", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "drone", "visualization", "change detection" ], "weblinks": [], "files": [ { "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", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/hamzic_ademir-baa/hamzic_ademir-baa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "hamzic_ademir-baa-thesis.pdf", "type": "application/pdf", "size": 849506, "path": "Publication:hamzic_ademir-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/hamzic_ademir-baa/hamzic_ademir-baa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/hamzic_ademir-baa/hamzic_ademir-baa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/hamzic_ademir-baa/", "__class": "Publication" }, { "id": "kubicek-2024-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Real-time Change Detection", "date": "2025-08-05", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 455, "image_height": 341, "name": "kubicek-2024-baa-image.png", "type": "image/png", "size": 184918, "path": "Publication:kubicek-2024-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/kubicek-2024-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/kubicek-2024-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5363 ], "date_end": "2025-08-05", "date_start": "2024-05-13", "matrikelnr": "12026660", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "change detection", "stereo vision", "data structure" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 455, "image_height": 341, "name": "kubicek-2024-baa-image.png", "type": "image/png", "size": 184918, "path": "Publication:kubicek-2024-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/kubicek-2024-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/kubicek-2024-baa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "kubicek-2024-baa-thesis.pdf", "type": "application/pdf", "size": 960689, "path": "Publication:kubicek-2024-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/kubicek-2024-baa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/kubicek-2024-baa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/kubicek-2024-baa/", "__class": "Publication" }, { "id": "kozonits_marcel-2025-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Optimize cyclesafely pipeline", "date": "2025-07-07", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "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" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5446 ], "co_supervisor": [ 948 ], "date_end": "2025-07-07", "date_start": "2025-02-20", "matrikelnr": "12122382", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "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": [ "WorldScale" ], "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": [ 5296 ], "date_end": "2025-03-28", "date_start": "2023-07-13", "matrikelnr": "11938253", "supervisor": [ 193, 948 ], "research_areas": [ "Geometry" ], "keywords": [ "drone", "scanning", "automation" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "name": "wiesinger_2025-baa-image.png", "type": "image/png", "size": 351129, "path": "Publication:wiesinger_2025-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/wiesinger_2025-baa/wiesinger_2025-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/wiesinger_2025-baa/wiesinger_2025-baa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "wiesinger_2025-baa-thesis.pdf", "type": "application/pdf", "size": 87944075, "path": "Publication:wiesinger_2025-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/wiesinger_2025-baa/wiesinger_2025-baa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/wiesinger_2025-baa/wiesinger_2025-baa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/wiesinger_2025-baa/", "__class": "Publication" }, { "id": "fuchs_peter-2024-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Efficient User Guidance to Next-Best-View Scan Positions", "date": "2025-02-28", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 149, "image_height": 100, "name": "fuchs_peter-2024-baa-image.png", "type": "image/png", "size": 26907, "path": "Publication:fuchs_peter-2024-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/fuchs_peter-2024-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/fuchs_peter-2024-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5381 ], "co_supervisor": [ 948 ], "date_end": "2025-02-28", "date_start": "2024-09-01", "matrikelnr": "12125442", "supervisor": [ 193 ], "research_areas": [ "Geometry" ], "keywords": [ "next-best-view", "user guidance", "scanning" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 149, "image_height": 100, "name": "fuchs_peter-2024-baa-image.png", "type": "image/png", "size": 26907, "path": "Publication:fuchs_peter-2024-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/fuchs_peter-2024-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/fuchs_peter-2024-baa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "fuchs_peter-2024-baa-thesis.pdf", "type": "application/pdf", "size": 10633086, "path": "Publication:fuchs_peter-2024-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/fuchs_peter-2024-baa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/fuchs_peter-2024-baa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/fuchs_peter-2024-baa/", "__class": "Publication" }, { "id": "steinheber-2024-oto", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Observation and Tracking of Plant Health using Computer Vision", "date": "2025-02-19", "abstract": "- select neural network for classification of diseases, dryness of soil etc. with multispectral analysis, with open source, input should 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": "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" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5354 ], "date_end": "2025-02-19", "date_start": "2024-04-19", "matrikelnr": "12022506", "note": "1", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "plants", "health monitoring", "computer vision" ], "weblinks": [], "files": [ { "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": "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": [ "WorldScale" ], "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": [ 1796 ], "ac_number": "AC17754544", "co_supervisor": [ 948 ], "date_start": "2021-11-29", "doi": "10.34726/hss.2025.111224", "matrikelnr": "01612401", "open_access": "yes", "pages": "86", "supervisor": [ 193 ], "research_areas": [ "Geometry" ], "keywords": [ "cycling", "collision detection", "trajectory prediction", "machine learning" ], "weblinks": [], "files": [ { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "pointner_simon-2022-maa-thesis.pdf", "type": "application/pdf", "size": 6379604, "path": "Publication:pointner_simon-2022-maa", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/pointner_simon-2022-maa/pointner_simon-2022-maa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/pointner_simon-2022-maa/pointner_simon-2022-maa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/pointner_simon-2022-maa/", "__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": "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": [ 5353 ], "date_end": "2024-08-12", "date_start": "2024-03-11", "matrikelnr": "12122086", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "bicycle", "mobile", "scanning" ], "weblinks": [], "files": [ { "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" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "hoffer-toth-com-thesis.pdf", "type": "application/pdf", "size": 6093633, "path": "Publication:hoffer-toth-com", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/hoffer-toth-com/hoffer-toth-com-thesis.pdf", "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-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/hoffer-toth-com/", "__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": "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": "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": [ 1825, 948, 823, 5297 ], "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": 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" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "parakkat-2024-ballmerge-paper.pdf", "type": "application/pdf", "size": 75917181, "path": "Publication:parakkat-2024-ballmerge", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/parakkat-2024-ballmerge/parakkat-2024-ballmerge-paper.pdf", "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-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "Superhumans", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/parakkat-2024-ballmerge/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2023-con", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Feature-Sized Sampling for Vector Line Art", "date": "2023-10-12", "abstract": "By introducing a first-of-its-kind quantifiable sampling algorithm based on feature size, we present a fresh perspective on the practical aspects of planar curve sampling. 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. ", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 550, "image_height": 286, "name": "ohrhallinger_stefan-2023-con-image.png", "type": "image/png", "size": 96497, "path": "Publication:ohrhallinger_stefan-2023-con", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/ohrhallinger_stefan-2023-con-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/ohrhallinger_stefan-2023-con-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 948, 1825, 5297 ], "booktitle": "Proceedings of the 31th Pacific Conference on Computer Graphics and Applications", "doi": "https://doi.org/10.2312/pg.20231268", "event": "Pacific Graphics, Daejeon, South Korea, 2023", "lecturer": [ 948 ], "open_access": "yes", "pages_from": "1", "pages_to": "2", "research_areas": [ "Geometry" ], "keywords": [ "sampling", "vector line art", "meshing" ], "weblinks": [ { "href": "https://youtu.be/-x3UujZKe9E?si=QfHIyzQ0hyP3PqM3", "caption": "talk", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 550, "image_height": 286, "name": "ohrhallinger_stefan-2023-con-image.png", "type": "image/png", "size": 96497, "path": "Publication:ohrhallinger_stefan-2023-con", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/ohrhallinger_stefan-2023-con-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/ohrhallinger_stefan-2023-con-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ohrhallinger_stefan-2023-con-paper.pdf", "type": "application/pdf", "size": 8132185, "path": "Publication:ohrhallinger_stefan-2023-con", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/ohrhallinger_stefan-2023-con-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/ohrhallinger_stefan-2023-con-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/ohrhallinger_stefan-2023-con/", "__class": "Publication" }, { "id": "withalm_brigitte-2022-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Transport Mode Detection Segmentation", "date": "2023-08-21", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": 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"public", "name": "withalm_brigitte-2022-baa-thesis.pdf", "type": "application/pdf", "size": 4351326, "path": "Publication:withalm_brigitte-2022-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/withalm_brigitte-2022-baa/withalm_brigitte-2022-baa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/withalm_brigitte-2022-baa/withalm_brigitte-2022-baa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/withalm_brigitte-2022-baa/", "__class": "Publication" }, { "id": "wiesinger_klemens-2023-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Using a Drone for Automated 3D Scanning", "date": "2023-07-04", "abstract": "Tasks für BA:\n- get drone to fly and transmit from raspi to server\n- transmit best coordinates (relative vector, relative angles) from output of 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": [ 5296 ], "date_end": "2025-03-28", "date_start": "2023-07-04", "matrikelnr": "011938253", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "drone flying", "client-server", "3d scanning" ], "weblinks": [], "files": [ { "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" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/wiesinger_klemens-2023-baa/", "__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": "rubik_michael-2022-baa", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "CycleSafely Acquire Data", "date": "2023-01-24", "abstract": "Anfang März: Anfang der Arbeit, Überblick über alle Schritte und Interfaces\nMitte März: Festlegen auf/Kaufen des Tiefensensors\nApril: Verarbeiten und Registrieren der Punktwolke\nMai: GPS Daten auslesen, übertragen und mit Punktwolke verknüpfen\nJuni: RGB Video der Smartphone Kamera mit Tiefenbildern verknüpfen\n Spielraum um nicht vollendete Aufgaben zu erledigen", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 525, "image_height": 407, "name": "rubik_michael-2022-baa-image.png", "type": "image/png", "size": 320696, "path": "Publication:rubik_michael-2022-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/rubik_michael-2022-baa/rubik_michael-2022-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/rubik_michael-2022-baa/rubik_michael-2022-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1918 ], "date_end": "2023-01-24", "date_start": "2022-03-01", "matrikelnr": "11809937", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "lidar", "point cloud", "registration" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 525, "image_height": 407, "name": "rubik_michael-2022-baa-image.png", "type": "image/png", "size": 320696, "path": "Publication:rubik_michael-2022-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/rubik_michael-2022-baa/rubik_michael-2022-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/rubik_michael-2022-baa/rubik_michael-2022-baa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": false, "use_in_gallery": true, "access": "public", "name": "rubik_michael-2022-baa-thesis.pdf", "type": "application/pdf", "size": 4799027, "path": "Publication:rubik_michael-2022-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/rubik_michael-2022-baa/rubik_michael-2022-baa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/rubik_michael-2022-baa/rubik_michael-2022-baa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "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": [ 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": "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": [ 5287 ], "date_end": "2022-09-05", "date_start": "2022-01-15", "matrikelnr": "11811340", "supervisor": [ 193, 948 ], "research_areas": [ "Geometry" ], "keywords": [ "point clouds", "object detection", "editing" ], "weblinks": [], "files": [ { "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" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "elagrod_ahmed-baa-thesis.pdf", "type": "application/pdf", "size": 18887807, "path": "Publication:elagrod_ahmed-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/elagrod_ahmed-baa/elagrod_ahmed-baa-thesis.pdf", "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-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/elagrod_ahmed-baa/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2022-it1", "type_id": "talk", "tu_id": null, "repositum_id": "20.500.12708/153673", "title": "Curve/Surface Reconstruction and Occlusion-enabled Applications", "date": "2022-05-02", "abstract": "Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple, and non-manifold outlines, 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. This permits novel real-time methods on large point clouds such as collision detection, surface processing like cutting or editing, and efficient exploration.\n\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 286, "image_height": 295, "name": "ohrhallinger_stefan-2022-it1-.JPG", "type": "image/jpeg", "size": 27869, "path": "Publication:ohrhallinger_stefan-2022-it1", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-it1/ohrhallinger_stefan-2022-it1-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-it1/ohrhallinger_stefan-2022-it1-:thumb{{size}}.png" }, "sync_repositum_override": "date,location,projects,abstract", "repositum_presentation_id": null, "authors": [ 948 ], "date_from": "2022-05-02", "date_to": "2022-05-02", "event": "Invited Talk", "lecturer": [ 948 ], "location": "LiX Polytechnique, Saclay-Paris, France", "research_areas": [], "keywords": [ "curve reconstruction", "surface reconstruction", "occlusions" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 286, "image_height": 295, "name": "ohrhallinger_stefan-2022-it1-.JPG", "type": "image/jpeg", "size": 27869, "path": "Publication:ohrhallinger_stefan-2022-it1", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-it1/ohrhallinger_stefan-2022-it1-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-it1/ohrhallinger_stefan-2022-it1-:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-it1/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2022-tut", "type_id": "otherreviewed", "tu_id": null, "repositum_id": null, "title": "Shape Reconstruction: From Theory to Practice", "date": "2022-04-25", "abstract": "Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 476, "image_height": 251, "name": "ohrhallinger_stefan-2022-tut-.JPG", "type": "image/jpeg", "size": 27189, "path": "Publication:ohrhallinger_stefan-2022-tut", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/ohrhallinger_stefan-2022-tut-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/ohrhallinger_stefan-2022-tut-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 948, 1825, 1828 ], "booktitle": "Eurographics 2022 - Tutorials", "research_areas": [ "Geometry" ], "keywords": [ "curve reconstruction", "region reconstruction", "benchmark" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/stef/curvebenchmark/", "caption": "website", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 476, "image_height": 251, "name": "ohrhallinger_stefan-2022-tut-.JPG", "type": "image/jpeg", "size": 27189, "path": "Publication:ohrhallinger_stefan-2022-tut", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/ohrhallinger_stefan-2022-tut-.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/ohrhallinger_stefan-2022-tut-:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger_stefan-2022-tut/", "__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" }, { "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": [ 1755 ], "date_end": "2022-01-24", "date_start": "2020-03-23", "matrikelnr": "01426150", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "change detection", "uncertainty", "3d scanning" ], "weblinks": [], "files": [ { "description": null, "filetitle": "bachelor thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "steinschauer-2020-baa-bachelor thesis.pdf", "type": "application/pdf", "size": 1772406, "path": "Publication:steinschauer-2020-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/steinschauer-2020-baa/steinschauer-2020-baa-bachelor thesis.pdf", "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-bachelor thesis:thumb{{size}}.png" }, { "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" } ], "projects_workgroups": [ "WorldScale" ], "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": [ 948, 1825, 1828 ], "date_from": "22-10-23", "date_to": "22-10-27", "event": "ECCV 2022", "lecturer": [ 948, 1825, 1828 ], "location": "Tel Aviv", "research_areas": [], "keywords": [ "outline shape reconstruction" ], "weblinks": [], "files": [], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/ohrhallinger-2022-e2t/", "__class": "Publication" }, { "id": "ohrhallinger-2021-egs", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/58727", "title": "2D Points Curve Reconstruction Survey and Benchmark", "date": "2021-03-09", "abstract": "Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation 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. Our benchmark framework is available online to permit reproducing the results, and easy integration of new algorithms.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 5275, "image_height": 1881, "name": "ohrhallinger-2021-egs-image.png", "type": "image/png", "size": 1212842, "path": "Publication:ohrhallinger-2021-egs", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 948, 1828, 1825, 1826, 1827 ], "cfp": { "name": "call.pdf", "type": "application/pdf", "error": "0", "size": "408947", "orig_name": "call.pdf", "ext": "pdf" }, "date_from": "2019", "date_to": "2021-03-09", "event": "Eurographics 2021", "journal": "Computer Graphics Forum", "lecturer": [ 948, 1828, 1825 ], "location": "Online", "open_access": "yes", "pages": "22", "pages_from": "611", "pages_to": "632", "publisher": "Eurographics Association", "volume": "1", "research_areas": [ "Geometry" ], "keywords": [ "curve reconstruction", "state-of-the-art report", "benchmark" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/stef/curvebenchmark/", "caption": "Website", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 5275, "image_height": 1881, "name": "ohrhallinger-2021-egs-image.png", "type": "image/png", "size": 1212842, "path": "Publication:ohrhallinger-2021-egs", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ohrhallinger-2021-egs-paper.pdf", "type": "application/pdf", "size": 4185295, "path": "Publication:ohrhallinger-2021-egs", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/ohrhallinger-2021-egs/ohrhallinger-2021-egs-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "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": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 924, "image_height": 813, "name": "depner_dennis-2020-baa-image.png", "type": "image/png", "size": 614710, "path": "Publication:depner_dennis-2020-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/depner_dennis-2020-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/depner_dennis-2020-baa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1749 ], "date_end": "2021-03-01", "date_start": "2020-03-16", "matrikelnr": "01632716", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "3d scanning", "scan integration", "geometry processing" ], "weblinks": [ { "href": "https://gitlab.cg.tuwien.ac.at/stef/scan-integration", "caption": "source", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 924, "image_height": 813, "name": "depner_dennis-2020-baa-image.png", "type": "image/png", "size": 614710, "path": "Publication:depner_dennis-2020-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/depner_dennis-2020-baa-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/depner_dennis-2020-baa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "depner_dennis-2020-baa-paper.pdf", "type": "application/pdf", "size": 34339857, "path": "Publication:depner_dennis-2020-baa", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/depner_dennis-2020-baa-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/depner_dennis-2020-baa-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/", "__class": "Publication" } ]