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In this work, we address the task of point cloud denoising using a novel framework adapting Diffusion Schrödinger bridges to unstructured data like point sets. Unlike previous works that predict point-wise displacements from point features or learned noise distributions, our method learns an optimal transport plan between paired point clouds. In experiments on object datasets such as the PU-Net dataset and real-world datasets like ScanNet++ and ARKitScenes, P2P-Bridge improves by a notable margin over existing methods. Although our method demonstrates promising results utilizing solely point coordinates, we demonstrate that incorporating additional features like RGB information and point-wise DINOV2 features further improves the results.Code and pretrained networks are available at https://github.com/matvogel/P2P-Bridge .

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

  • 会议论文

  • 发表日期：

    2025年

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  • Mathias Vogel,
  • Keisuke Tateno,
  • Marc Pollefeys,
  • Federico Tombari,
  • Marie-Julie Rakotosaona,
  • Francis Engelmann

摘要

In this work, we address the task of point cloud denoising using a novel framework adapting Diffusion Schrödinger bridges to unstructured data like point sets. Unlike previous works that predict point-wise displacements from point features or learned noise distributions, our method learns an optimal transport plan between paired point clouds. In experiments on object datasets such as the PU-Net dataset and real-world datasets like ScanNet++ and ARKitScenes, P2P-Bridge improves by a notable margin over existing methods. Although our method demonstrates promising results utilizing solely point coordinates, we demonstrate that incorporating additional features like RGB information and point-wise DINOV2 features further improves the results.Code and pretrained networks are available at https://github.com/matvogel/P2P-Bridge .