Progressive Correspondence Regenerator for Robust 3D Registration

TL;DR

The paper introduces Regor, a progressive correspondence regenerator that improves 3D registration by generating high-quality matches through iterative local and global refinement, outperforming existing outlier removal methods.

Contribution

We propose a novel progressive approach that corrects and refines correspondences iteratively, enabling robust 3D registration with many outliers, unlike traditional outlier removal techniques.

Findings

Regor outperforms existing outlier removal methods in experiments.

It achieves 10 times more correct correspondences than traditional methods.

The approach is effective on both indoor and outdoor datasets.

Abstract

Obtaining enough high-quality correspondences is crucial for robust registration. Existing correspondence refinement methods mostly follow the paradigm of outlier removal, which either fails to correctly identify the accurate correspondences under extreme outlier ratios, or select too few correct correspondences to support robust registration. To address this challenge, we propose a novel approach named Regor, which is a progressive correspondence regenerator that generates higher-quality matches whist sufficiently robust for numerous outliers. In each iteration, we first apply prior-guided local grouping and generalized mutual matching to generate the local region correspondences. A powerful center-aware three-point consistency is then presented to achieve local correspondence correction, instead of removal. Further, we employ global correspondence refinement to obtain accurate correspondences from a global perspective. Through progressive iterations, this process yields a large number of high-quality correspondences. Extensive experiments on both indoor and outdoor datasets demonstrate that the proposed Regor significantly outperforms existing outlier removal techniques. More critically, our approach obtain 10 times more correct correspondences than outlier removal methods. As a result, our method is able to achieve robust registration even with weak features. The code will be released.