HECPG: Hyperbolic Embedding and Confident Patch-Guided Network for Point Cloud Matching

As a fundamental problem in photogrammetry and remote sensing, terrestrial laser scanner point cloud matching aims to seek a correspondence set that can match two partially overlapping point clouds. However, existing methods suffer from poor performance when dealing with low overlapping scenarios, and they also lack the ability to effectively utilize cross-space information. In this article, we propose a novel hyperbolic embedding and confident patch-guided (HECPG) network for point cloud matching. Our method leverages hyperbolic information to enhance feature representations and suppresses the effects of nonoverlapping regions through confidence guidance. Specifically, we first introduce hyperbolic attention, which effectively incorporates hyperbolic information into point cloud matching by leveraging the inherent hierarchical structure of point clouds. In addition, we propose a confident patch search (CPS) module that assigns a confidence score to each patch point. This helps to suppress outlier correspondences that may arise in nonoverlapping regions. Once we have obtained a set of high-precision point correspondences, we use the RANSAC algorithm to estimate the alignment transform for rigid point cloud registration. Extensive experiments on indoor, outdoor, and synthetic benchmarks demonstrate the superior performance of our HECPG. The code will be available at
https://github.com/IvanXie416/HECPG
.