Overlap Bias Matching is Necessary for Point Cloud Registration

TL;DR

This paper introduces OBMNet, an unsupervised network that effectively handles partial point cloud registration with low overlap by predicting overlap bias and improving correspondence matching.

Contribution

The paper proposes a novel plug-and-play Overlap Bias Matching Module (OBMM) integrated into OBMNet for better overlap estimation and correspondence in partial point cloud registration.

Findings

Significant performance improvement over state-of-the-art methods

Effective in scenarios with low overlap ratios

Robust partial registration across diverse datasets

Abstract

Point cloud registration is a fundamental problem in many domains. Practically, the overlap between point clouds to be registered may be relatively small. Most unsupervised methods lack effective initial evaluation of overlap, leading to suboptimal registration accuracy. To address this issue, we propose an unsupervised network Overlap Bias Matching Network (OBMNet) for partial point cloud registration. Specifically, we propose a plug-and-play Overlap Bias Matching Module (OBMM) comprising two integral components, overlap sampling module and bias prediction module. These two components are utilized to capture the distribution of overlapping regions and predict bias coefficients of point cloud common structures, respectively. Then, we integrate OBMM with the neighbor map matching module to robustly identify correspondences by precisely merging matching scores of points within the neighborhood, which addresses the ambiguities in single-point features. OBMNet can maintain efficacy even in pair-wise registration scenarios with low overlap ratios. Experimental results on extensive datasets demonstrate that our approach's performance achieves a significant improvement compared to the state-of-the-art registration approach.