Geometry‐Aware Correspondence Learning for Sparse and Low‐Overlap LiDAR Point Cloud Registration

Accurate registration of sparse and low‐overlap LiDAR point clouds plays an essential role in photogrammetric mapping, 3D scene reconstruction and geospatial data analysis. However, existing learning‐based registration methods typically perform keypoint detection and feature description as separate tasks, resulting in inconsistent feature representations and unreliable correspondences in challenging outdoor environments. To address this issue, this paper presents a geometry‐aware correspondence learning framework (GACNet) that jointly optimizes keypoint detection and descriptor extraction within a unified multi‐task architecture. Specifically, a lightweight geometry‐aware module is designed to integrate positional encoding with geometric self‐attention, enabling effective modeling of long‐range geometric dependencies and enhancing feature discriminability. In addition, a shared feature extraction backbone with task‐specific prediction heads simultaneously learns keypoint saliency and local descriptors, promoting consistency between keypoint localization and feature representation. Extensive experiments conducted on the 3DMatch and KITTI Odometry benchmarks demonstrate that the proposed method achieves superior registration performance and robustness compared with state‐of‐the‐art approaches while maintaining high computational efficiency. In particular, GACNet achieved 100% registration recall on the challenging KITTI Odometry dataset. The results indicate that the proposed framework provides an effective solution for large‐scale LiDAR point cloud registration and 3D geospatial applications in complex real‐world environments.