Diffusion Bridges for 3D Point Cloud Denoising
Abstract
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, 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.
Cite
Text
Hüni et al. "Diffusion Bridges for 3D Point Cloud Denoising." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-72627-9_11Markdown
[Hüni et al. "Diffusion Bridges for 3D Point Cloud Denoising." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/huni2024eccv-diffusion/) doi:10.1007/978-3-031-72627-9_11BibTeX
@inproceedings{huni2024eccv-diffusion,
title = {{Diffusion Bridges for 3D Point Cloud Denoising}},
author = {Hüni, Mathias Vogel and Tateno, Keisuke and Pollefeys, Marc and Tombari, Federico and Rakotosaona, Marie-Julie and Engelmann, Francis},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-72627-9_11},
url = {https://mlanthology.org/eccv/2024/huni2024eccv-diffusion/}
}