A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

TL;DR

This paper introduces a novel outlier removal strategy for point cloud registration that leverages the reliability of correspondence graphs, significantly improving speed and accuracy even with high outlier ratios.

Contribution

The authors propose a new outlier removal method based on the reliability degrees of nodes and edges in correspondence graphs, enhancing registration robustness and efficiency.

Findings

Effective outlier removal with over 99% outlier ratio

Improved registration accuracy and speed

Outperforms state-of-the-art methods in experiments

Abstract

Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph. The method constructs the corresponding graph according to the given correspondences and designs the concept of the reliability degree of the graph node for optimal candidate selection and the reliability degree of the graph edge to obtain the global maximum consensus set. The presented method could achieve fast and accurate outliers removal along with gradual aligning parameters estimation. Extensive experiments on simulations and challenging real-world datasets demonstrate that the proposed method can still perform effective point cloud registration even the correspondence outlier ratio is over 99%, and the efficiency is better than the state-of-the-art. Code is available at https://github.com/WPC-WHU/GROR.