iMatcher：通过局部到全局几何一致性学习改进点云配准中的匹配
iMatcher: Improve matching in point cloud registration via local-to-global geometric consistency learning
AI总结：
iMatcher通过局部到全局几何一致性学习改进点云配准中的特征匹配，实现高内点率与鲁棒刚性配准。
摘要：
本文提出了iMatcher，一个用于点云配准中特征匹配的全可微框架。所提出的方法利用学习到的特征来预测一个几何一致的置信度矩阵，同时结合了局部和全局一致性。首先，一个局部图嵌入模块引导得分矩阵的初始化。随后，一个重定位步骤通过考虑在3D空间中基于最近邻搜索的双向源到目标和目标到源匹配来细化该矩阵。然后，将配对的点特征堆叠起来，通过全局几何一致性学习进行细化，以预测逐点的匹配概率。在真实世界室外数据集（KITTI、KITTI-360）和室内数据集（3DMatch）以及6自由度姿态估计（TUD-L）和部分到部分匹配（MVP-RG）上的大量实验表明，iMatcher显著提高了刚性配准性能。该方法实现了最先进的内点比率，在KITTI上达到95%至97%，在KITTI-360上达到94%至97%，在3DMatch上高达81.1%，突显了其在多种场景下的鲁棒性。
英文摘要：
This paper presents iMatcher, a fully differentiable framework for feature matching in point cloud registration. The proposed method leverages learned features to predict a geometrically consistent confidence matrix, incorporating both local and global consistency. First, a local graph embedding module leads to an initialization of the score matrix. A subsequent repositioning step refines this matrix by considering bilateral source-to-target and target-to-source matching via nearest neighbor search in 3D space. The paired point features are then stacked together to be refined through global geometric consistency learning to predict a point-wise matching probability. Extensive experiments on real-world outdoor (KITTI, KITTI-360) and indoor (3DMatch) datasets, as well as on 6-DoF pose estimation (TUD-L) and partial-to-partial matching (MVP-RG), demonstrate that iMatcher significantly improves rigid registration performance. The method achieves state-of-the-art inlier ratios, scoring 95% - 97% on KITTI, 94% - 97% on KITTI-360, and up to 81.1% on 3DMatch, highlighting its robustness across diverse settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
