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Jianshi Wu

1 accepted papers

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

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