Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908669928538112 |
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| author | Ehm, Viktoria Amrani, Nafie El Xie, Yizheng Bastian, Lennart Gao, Maolin Wang, Weikang Sang, Lu Cao, Dongliang Weißberg, Tobias Lähner, Zorah Cremers, Daniel Bernard, Florian |
| author_facet | Ehm, Viktoria Amrani, Nafie El Xie, Yizheng Bastian, Lennart Gao, Maolin Wang, Weikang Sang, Lu Cao, Dongliang Weißberg, Tobias Lähner, Zorah Cremers, Daniel Bernard, Florian |
| contents | Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape matching datasets exist, they are mostly static or limited in size, restricting their adaptation to different problem settings, including both full and partial shape matching. In particular the existing partial shape matching datasets are small (fewer than 100 shapes) and thus unsuitable for data-hungry machine learning approaches. Moreover, the type of partiality present in existing datasets is often artificial and far from realistic. To address these limitations, we introduce a generic and flexible framework for the procedural generation of challenging full and partial shape matching datasets. Our framework allows the propagation of custom annotations across shapes, making it useful for various applications. By utilising our framework and manually creating cross-dataset correspondences between seven existing (complete geometry) shape matching datasets, we propose a new large benchmark BeCoS with a total of 2543 shapes. Based on this, we offer several challenging benchmark settings, covering both full and partial matching, for which we evaluate respective state-of-the-art methods as baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms Ehm, Viktoria Amrani, Nafie El Xie, Yizheng Bastian, Lennart Gao, Maolin Wang, Weikang Sang, Lu Cao, Dongliang Weißberg, Tobias Lähner, Zorah Cremers, Daniel Bernard, Florian Computer Vision and Pattern Recognition Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape matching datasets exist, they are mostly static or limited in size, restricting their adaptation to different problem settings, including both full and partial shape matching. In particular the existing partial shape matching datasets are small (fewer than 100 shapes) and thus unsuitable for data-hungry machine learning approaches. Moreover, the type of partiality present in existing datasets is often artificial and far from realistic. To address these limitations, we introduce a generic and flexible framework for the procedural generation of challenging full and partial shape matching datasets. Our framework allows the propagation of custom annotations across shapes, making it useful for various applications. By utilising our framework and manually creating cross-dataset correspondences between seven existing (complete geometry) shape matching datasets, we propose a new large benchmark BeCoS with a total of 2543 shapes. Based on this, we offer several challenging benchmark settings, covering both full and partial matching, for which we evaluate respective state-of-the-art methods as baselines. |
| title | Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.03511 |