Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning

Ray Zhang, Zheming Zhou, Min Sun, Omid Ghasemalizadeh, Cheng-Hao Kuo, Ryan M. Eustice, Maani Ghaffari Jadidi, Arnie Sen

ECCV 2024

doi:10.1007/978-3-031-73223-2_5 /eccv/2024/zhang2024eccv-correspondencefree/

Abstract

This paper introduces a robust unsupervised SE(3) point cloud registration method that operates without requiring point correspondences. The method frames point clouds as functions in a reproducing kernel Hilbert space (RKHS), leveraging SE(3)-equivariant features for direct feature space registration. A novel RKHS distance metric is proposed, offering reliable performance amidst noise, outliers, and asymmetrical data. An unsupervised training approach is introduced to effectively handle limited ground truth data, facilitating adaptation to real datasets. The proposed method outperforms classical and supervised methods in terms of registration accuracy on both synthetic (ModelNet40) and real-world (ETH3D) noisy, outlier-rich datasets. To our best knowledge, this marks the first instance of successful real RGB-D odometry data registration using an equivariant method. The code is available at https://sites.google. com/view/eccv24-equivalign.

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Cite

Text

Zhang et al. "Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73223-2_5

Markdown

[Zhang et al. "Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/zhang2024eccv-correspondencefree/) doi:10.1007/978-3-031-73223-2_5

BibTeX

@inproceedings{zhang2024eccv-correspondencefree,
  title     = {{Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning}},
  author    = {Zhang, Ray and Zhou, Zheming and Sun, Min and Ghasemalizadeh, Omid and Kuo, Cheng-Hao and Eustice, Ryan M. and Jadidi, Maani Ghaffari and Sen, Arnie},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2024},
  doi       = {10.1007/978-3-031-73223-2_5},
  url       = {https://mlanthology.org/eccv/2024/zhang2024eccv-correspondencefree/}
}