上海市医学图像处理与计算机辅助手术重点实验室

上海市医学图像处理与计算机辅助手术重点实验室

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    [TPAMI] Robust Point Cloud Registration Framework Based on Deep Graph Matching

    发表时间:2022-09-05

    Robust Point Cloud Registration Framework Based on Deep Graph Matching

    Kexue Fu*, Jiazheng Luo*, Xiaoyuan Luo, Shaolei Liu, Chenxi Zhang, Manning Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence (IF=24.314)

    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: https://github.com/fukexue/RGM