P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
<|reference_start|>P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising: 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.<|reference_end|>
@article{vogel2024p2p-bridge:,
    title={P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising},
    author={Mathias Vogel, Keisuke Tateno, Marc Pollefeys, Federico Tombari,
  Marie-Julie Rakotosaona, Francis Engelmann},
    journal={arXiv preprint arXiv:2408.16325},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16325},
    primaryClass={cs.CV}
}