Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

• URL: http://arxiv.org/abs/2211.04696v1
• Date: Wed, 9 Nov 2022 06:05:25 GMT
• Title: Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)
• Authors: Kexue Fu, Jiazheng Luo, Xiaoyuan Luo, Shaolei Liu, Chenxi Zhang, Manning Wang
• Abstract summary: 3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matching-based framework for point cloud registration.
• Score: 13.286247750893681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: \href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.

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