GCML: Geometric Correlation Encoding Network With Multi-Scale Local Feature Extraction for Accurate Point Cloud Registration

Point cloud registration, a critical component of various applications, aims to establish correspondences between two point clouds. While detector-free methods exhibit outstanding accuracy, they only encode simple geometric features of point clouds while failing to comprehensively model rich geometric correlations between points. Moreover, they treat point clouds as sequential data and only employ the transformer to perform global message propagation during feature enhancement. This approach inevitably neglects the extraction of multi-scale local features, thereby compromising the descriptive capability of point cloud features. This study introduces GCML, a novel detector-free approach that tackles these issues. For the first problem, GCML develops a geometric correlation encoding module (GCEM) that draws inspiration from the Denavit-Hartenberg (DH) modeling method in robotics to effectively encode the geometric correlations between each pair of points within point clouds. This method substantially captures rich geometric structure information inherent in point clouds, thereby improving registration accuracy. For the second problem, GCML proposes a multi-scale local feature perception module (MLFP) that integrates multi-scale local features of point clouds during the feature enhancement stage. Subsequently, GCML employs the transformer architecture to facilitate global message propagation. This design effectively captures both local and global features of point clouds, hence enhancing the descriptive capability of point cloud features. Extensive experiments demonstrate that GCML achieves superior performance on all ModelNet40, DModelNet40, and KITTI datasets.