Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets

2026-04-29

AI Summary

This work addresses the challenge of balancing accuracy and efficiency in 3D point cloud registration for remote sensing applications by proposing a fast registration algorithm based on probabilistic self-updating local correspondences and line vector sets. The method employs a dual-RANSAC interactive model to jointly optimize global and dynamically refined local correspondences, constructs a robust initial structure using angle histograms and line-length preservation, and incorporates a probabilistic self-updating mechanism along with a global early-stopping strategy to balance precision and computational cost. The optimal transformation is finally estimated via weighted singular value decomposition. Experimental results demonstrate that the proposed approach reduces root mean square error by at least 10% compared to state-of-the-art methods on public datasets while achieving significantly faster runtime.

Abstract

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. To improve accuracy, a probabilistic self-updating strategy refines the local sets after each interaction round. To reduce runtime, we introduce a global early termination condition that optimally balances accuracy and efficiency. Finally, a weighted singular value decomposition estimates the registration solution. Evaluations on public datasets demonstrate our algorithm achieves superior time efficiency and at least a 10% root mean square error improvement over state-of-the-art methods. The C++ source code is publicly available at https://github.com/ivpml84079/Probabilistic-Self-Update-Line-Vector-Set-Based-Point-Cloud-Registration.

Problem

• Point Cloud Registration
• Remote Sensing
• 3D Data Integration
• Correspondence Estimation
• Registration Accuracy

Innovation

• probabilistic self-update
• line vector sets
• dual RANSAC
• point cloud registration
• early termination