GS-Matching: Reconsidering Feature Matching task in Point Cloud Registration
This work addresses feature matching inefficiencies in point cloud registration for applications like robotics and 3D reconstruction, though it is incremental as it builds on existing assignment problem approaches.
The paper tackles the problem of many-to-one matches and missing correspondences in point cloud registration by proposing GS-matching, a stable matching policy inspired by the Gale-Shapley algorithm, which achieves better registration recall on multiple datasets under low overlapping conditions.
Traditional point cloud registration (PCR) methods for feature matching often employ the nearest neighbor policy. This leads to many-to-one matches and numerous potential inliers without any corresponding point. Recently, some approaches have framed the feature matching task as an assignment problem to achieve optimal one-to-one matches. We argue that the transition to the Assignment problem is not reliable for general correspondence-based PCR. In this paper, we propose a heuristics stable matching policy called GS-matching, inspired by the Gale-Shapley algorithm. Compared to the other matching policies, our method can perform efficiently and find more non-repetitive inliers under low overlapping conditions. Furthermore, we employ the probability theory to analyze the feature matching task, providing new insights into this research problem. Extensive experiments validate the effectiveness of our matching policy, achieving better registration recall on multiple datasets.