2026
LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization
CVPR 2026
LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-based regression methods offer efficient solutions by directly predicting global poses without the need for explicit map storage. However, these method