A strengthened soft-point registration network for LiDAR point cloud registration

Abstract

In complex environments, fast and accurate registration of LiDAR point clouds is crucial for ensuring the safety of robot environmental perception and other LiDAR-based applications. Existing point cloud registration methods typically rely on feature matching to find correspondences between points and use RANSAC to estimate transformation parameters after filtering out incorrect correspondence\s. However, this approach introduces two problems: first, in partial registration scenarios, where point cloud shapes are incomplete, feature matching becomes unreliable. Second, the significant number of iterations required by RANSAC increases the time needed for registration. To address these issues, this paper introduces a strengthened soft-point registration network (SPRN) based on cross-attention—an end-to-end registration network that is feature-matching-free and RANSAC-free. First, the model independently generates k soft points for the source and target point clouds, producing k point-to-point correspondences. The rigid transformation parameters are then directly estimated from these k correspondences. Each soft point is obtained by a weighted aggregation of all superpoints in the source or target cloud, where the weights are automatically assigned by the attention mechanism. Although the local geometric structures at the corresponding locations may differ due to incompleteness, the attention mechanism focuses on the shared parts and filters out the inconsistent ones, making these soft-point correspondences more reliable than those obtained via feature matching. Moreover, our method avoids the feature-matching and RANSAC steps, resulting in a much faster runtime. Experiments conducted on the real-world datasets 3DMatch, 3DLoMatch, KITTI, and WHU-TLS demonstrate that this method achieves faster point cloud registration while maintaining registration accuracy.