通过几何一致性学习提升点云匹配精度，显著改善注册效果。
iMatcher: Improve matching in point cloud registration via local-to-global geometric consistency learning
- 构建局部到全局一致性的可微分匹配框架
- 在KITTI等数据集上达到95%以上正确匹配率
- 适合需要高精度点云配准的自动驾驶与三维重建场景
本文提出iMatcher，一种用于点云配准中特征匹配的全可微分框架。该方法利用学习到的特征预测几何一致的置信度矩阵，融合局部与全局一致性。首先通过局部图嵌入模块初始化得分矩阵；随后通过3D空间中的最近邻搜索，进行源到目标和目标到源的双向匹配优化；再将配对点特征堆叠并经由全局几何一致性学习，预测逐点匹配概率。在真实世界户外（KITTI、KITTI-360）和室内（3DMatch）数据集，以及6-DoF姿态估计（TUD-L）和部分到部分匹配（MVP-RG）任务上的大量实验表明，iMatcher显著提升刚性配准性能。其在KITTI上实现95%-97%的内点率，在KITTI-360上达94%-97%，在3DMatch上最高达81.1%，展现出跨场景强鲁棒性。
原文摘要 · Abstract (English)
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.
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