iMatcher: Improve matching in point cloud registration via local-to-global geometric consistency learning

2025-09-10

AI Summary

To address the lack of global geometric consistency constraints in local feature matching for point cloud registration, this paper proposes iMatcher—a fully differentiable, end-to-end matching framework. Its core contributions are threefold: (1) constructing local graph embeddings to explicitly model neighborhood structural relationships; (2) introducing a bidirectional source–target matching relocation mechanism to enhance the robustness of initial correspondences; and (3) designing a global geometric consistency learning module that jointly optimizes match confidence scores and rigid-body transformation constraints. Evaluated on KITTI, KITTI-360, and 3DMatch benchmarks, iMatcher achieves inlier ratios of 95–97%, 94–97%, and 81.1%, respectively—substantially outperforming state-of-the-art methods. These results validate the effectiveness of synergistic local–global modeling for robust and accurate point cloud registration.

Technology Category

Intelligent Robots: Localization, Mapping, and NavigationComputer Vision: Motion & TrackingMachine Learning: Learning with Manifolds

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Improve point cloud registration matching accuracy
• Enhance local-to-global geometric consistency learning
• Boost rigid registration performance across diverse datasets

Innovation

• Learned features predict geometric consistency confidence
• Local graph embedding initializes score matrix
• Global geometric consistency refines matching probability