Robust Point Cloud Registration via Patch Matching

Tianming Zhao, Tian Tian, Xu Zou, Luxin Yan, Sheng Zhong

Abstract: We study the problem of exacting accurate correspondence pairs for point cloud registration. The existing correspondence methods focus on constructing point descriptors and then extracting correspondence point pairs. However, this process encounters two main issues: 1) point features are unstable and susceptible to noise, leading to a low inlier ratio (IR) for correspondence pairs and 2) the positional deviation of correspondence point pair results in accuracy errors when computing rigid transformations. To address these issues, we propose a robust point cloud registration framework based on patch matching, achieving high positional accuracy and high inlier-rate prediction of correspondence pairs. Specifically, we design a dual-branch point cloud registration network, with one branch dedicated to patch matching and the other branch to predicting the patch anchor, i.e., the coordinates used for patch matching. For patch matching, we integrate the topology of patches into the attention mechanism and adopt a multilevel patch-matching strategy to enhance the matching success rate. For coordinate prediction, we introduce graph convolutional network (GCN) and cross-attention mechanisms to explore local similar points through information interaction and feature correlation of patch pairs. Thanks to the stability of patch descriptors, our method demonstrates higher robustness compared to existing correspondence methods. Extensive experiments conducted on indoor, outdoor, synthetic, and deformable benchmarks validate the superiority of our method. Additionally, our method achieves certain effectiveness in cross-source point clouds.