Geometric Transformer for Fast and Robust Point Cloud Registration

• URL: http://arxiv.org/abs/2202.06688v1
• Date: Mon, 14 Feb 2022 13:26:09 GMT
• Title: Geometric Transformer for Fast and Robust Point Cloud Registration
• Authors: Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng and Kai Xu
• Abstract summary: We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios. We propose Geometric Transformer to learn geometric feature for robust superpoint matching.
• Score: 53.10568889775553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it robust in low-overlap cases and invariant to rigid transformation. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to $100$ times acceleration. Our method improves the inlier ratio by 17\%$\sim$30\% and the registration recall by over 7\% on the challenging 3DLoMatch benchmark. The code and models will be released at \url{https://github.com/qinzheng93/GeoTransformer}.

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