P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916374445555712 |
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| author | Vogel, Mathias Tateno, Keisuke Pollefeys, Marc Tombari, Federico Rakotosaona, Marie-Julie Engelmann, Francis |
| author_facet | Vogel, Mathias Tateno, Keisuke Pollefeys, Marc Tombari, Federico Rakotosaona, Marie-Julie Engelmann, Francis |
| contents | In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schrödinger bridges to points clouds. Unlike previous approaches that predict point-wise displacements from point features or learned noise distributions, our method learns an optimal transport plan between paired point clouds. Experiments on object datasets like PU-Net and real-world datasets such as ScanNet++ and ARKitScenes show that P2P-Bridge achieves significant improvements over existing methods. While our approach demonstrates strong results using only point coordinates, we also show that incorporating additional features, such as color information or point-wise DINOv2 features, further enhances the performance. Code and pretrained models are available at https://p2p-bridge.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16325 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising Vogel, Mathias Tateno, Keisuke Pollefeys, Marc Tombari, Federico Rakotosaona, Marie-Julie Engelmann, Francis Computer Vision and Pattern Recognition In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schrödinger bridges to points clouds. Unlike previous approaches that predict point-wise displacements from point features or learned noise distributions, our method learns an optimal transport plan between paired point clouds. Experiments on object datasets like PU-Net and real-world datasets such as ScanNet++ and ARKitScenes show that P2P-Bridge achieves significant improvements over existing methods. While our approach demonstrates strong results using only point coordinates, we also show that incorporating additional features, such as color information or point-wise DINOv2 features, further enhances the performance. Code and pretrained models are available at https://p2p-bridge.github.io. |
| title | P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.16325 |