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

• URL: http://arxiv.org/abs/2407.20223v1
• Date: Mon, 29 Jul 2024 17:57:38 GMT
• Title: Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning
• Authors: Ray Zhang, Zheming Zhou, Min Sun, Omid Ghasemalizadeh, Cheng-Hao Kuo, Ryan Eustice, Maani Ghaffari, Arnie Sen,
• Abstract summary: This paper introduces a robust unsupervised SE(3) point cloud registration method that operates without requiring point correspondences. A novel RKHS distance metric is proposed, offering reliable performance amidst noise, outliers, and asymmetrical data. 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.
• Score: 13.098807543505028
• License: http://creativecommons.org/licenses/by/4.0/
• 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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