AI RESEARCH
Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets
arXiv CS.CV
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ArXi:2604.26318v1 Announce Type: new Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effective PCR algorithm utilizing probabilistic self-updating local correspondence and line vector sets. Our dual RANSAC interaction model comprises a global RANSAC evaluating the global correspondence set and a local RANSAC operating on dynamically updated local sets. Initially, these local sets are constructed using angle histogram statistics and line vector length preservation techniques.