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

• URL: http://arxiv.org/abs/2408.16325v1
• Date: Thu, 29 Aug 2024 08:00:07 GMT
• Title: P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
• Authors: Mathias Vogel, Keisuke Tateno, Marc Pollefeys, Federico Tombari, Marie-Julie Rakotosaona, Francis Engelmann,
• Abstract summary: We tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr"odinger bridges to points clouds. Experiments on object datasets show that P2P-Bridge achieves significant improvements over existing methods.
• Score: 81.92854168911704
• License: http://creativecommons.org/licenses/by/4.0/
• 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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