Implicit Neural Surface Deformation with Explicit Velocity Fields
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915119544401920 |
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| author | Sang, Lu Canfes, Zehranaz Cao, Dongliang Bernard, Florian Cremers, Daniel |
| author_facet | Sang, Lu Canfes, Zehranaz Cao, Dongliang Bernard, Florian Cremers, Daniel |
| contents | In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propose to model the point movement using an explicit velocity field and directly deform a time-varying implicit field using the modified level-set equation. This equation utilizes an iso-surface evolution with Eikonal constraints in a compact formulation, ensuring the integrity of the signed distance field. By applying a smooth, volume-preserving constraint to the velocity field, our method successfully recovers physically plausible intermediate shapes. Our method is able to handle both rigid and non-rigid deformations without any intermediate shape supervision. Our experimental results demonstrate that our method significantly outperforms existing works, delivering superior results in both quality and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14038 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Implicit Neural Surface Deformation with Explicit Velocity Fields Sang, Lu Canfes, Zehranaz Cao, Dongliang Bernard, Florian Cremers, Daniel Computer Vision and Pattern Recognition In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propose to model the point movement using an explicit velocity field and directly deform a time-varying implicit field using the modified level-set equation. This equation utilizes an iso-surface evolution with Eikonal constraints in a compact formulation, ensuring the integrity of the signed distance field. By applying a smooth, volume-preserving constraint to the velocity field, our method successfully recovers physically plausible intermediate shapes. Our method is able to handle both rigid and non-rigid deformations without any intermediate shape supervision. Our experimental results demonstrate that our method significantly outperforms existing works, delivering superior results in both quality and efficiency. |
| title | Implicit Neural Surface Deformation with Explicit Velocity Fields |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.14038 |