提出新匹配策略，在低重叠下更准地找点云对应关系。
GS-Matching: Reconsidering Feature Matching task in Point Cloud Registration
- 基于戈尔-沙普利算法设计启发式匹配，避免多对一问题。
- 在低重叠条件下找到更多无重复的正确匹配点。
- 用概率分析揭示匹配本质，适合点云配准研究者参考。
传统点云配准（PCR）方法常采用最近邻策略，导致多对一匹配及大量无对应点的潜在内点。近期部分方法将特征匹配视为分配问题以实现最优一对一匹配。本文认为该转变在通用对应型PCR中不可靠。为此，我们提出一种受戈尔-沙普利算法启发的启发式稳定匹配策略——GS-matching。相比其他匹配策略，本方法在低重叠条件下可高效识别更多非重复内点。此外，我们引入概率理论分析特征匹配任务，为该问题提供新视角。大量实验证明，所提匹配策略在多个数据集上显著提升注册召回率。
原文摘要 · Abstract (English)
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.
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