DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909264264560640 |
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| author | Yao, Yuxin Ren, Siyu Hou, Junhui Deng, Zhi Zhang, Juyong Wang, Wenping |
| author_facet | Yao, Yuxin Ren, Siyu Hou, Junhui Deng, Zhi Zhang, Juyong Wang, Wenping |
| contents | This paper explores the problem of reconstructing temporally consistent surfaces from a 3D point cloud sequence without correspondence. To address this challenging task, we propose DynoSurf, an unsupervised learning framework integrating a template surface representation with a learnable deformation field. Specifically, we design a coarse-to-fine strategy for learning the template surface based on the deformable tetrahedron representation. Furthermore, we propose a learnable deformation representation based on the learnable control points and blending weights, which can deform the template surface non-rigidly while maintaining the consistency of the local shape. Experimental results demonstrate the significant superiority of DynoSurf over current state-of-the-art approaches, showcasing its potential as a powerful tool for dynamic mesh reconstruction. The code is publicly available at https://github.com/yaoyx689/DynoSurf. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction Yao, Yuxin Ren, Siyu Hou, Junhui Deng, Zhi Zhang, Juyong Wang, Wenping Computer Vision and Pattern Recognition This paper explores the problem of reconstructing temporally consistent surfaces from a 3D point cloud sequence without correspondence. To address this challenging task, we propose DynoSurf, an unsupervised learning framework integrating a template surface representation with a learnable deformation field. Specifically, we design a coarse-to-fine strategy for learning the template surface based on the deformable tetrahedron representation. Furthermore, we propose a learnable deformation representation based on the learnable control points and blending weights, which can deform the template surface non-rigidly while maintaining the consistency of the local shape. Experimental results demonstrate the significant superiority of DynoSurf over current state-of-the-art approaches, showcasing its potential as a powerful tool for dynamic mesh reconstruction. The code is publicly available at https://github.com/yaoyx689/DynoSurf. |
| title | DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.11586 |