Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension

arXiv:2403.03532v1 Announce Type: new
Abstract: Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global …

abstract acquisition applications arxiv cloud cs.cv driving extension literature registration safety type unsupervised vehicles view vital