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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03459 |
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| _version_ | 1866911250317836288 |
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| author | Manogue, Kevin Schang, Tomasz M Kuş, Dilara Müller, Jonas Zachow, Stefan Sengupta, Agniva |
| author_facet | Manogue, Kevin Schang, Tomasz M Kuş, Dilara Müller, Jonas Zachow, Stefan Sengupta, Agniva |
| contents | Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizing Shape-from-Template to Topological Changes Manogue, Kevin Schang, Tomasz M Kuş, Dilara Müller, Jonas Zachow, Stefan Sengupta, Agniva Computer Vision and Pattern Recognition Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. |
| title | Generalizing Shape-from-Template to Topological Changes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.03459 |