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arXiv · 2408.16325

P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising

Mathias Vogel·Keisuke Tateno·Marc Pollefeys·Federico Tombari·Marie-Julie Rakotosaona·Francis Engelmann
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Abstract

In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr\"odinger 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.

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Mathias Vogel, Keisuke Tateno, Marc Pollefeys, Federico Tombari, Marie-Julie Rakotosaona, Francis Engelmann. 2024-08-29. P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising. https://arxiv.org/abs/2408.16325

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