Overlap Bias Matching is Necessary for Point Cloud Registration
This addresses a practical limitation in point cloud registration for domains like robotics or 3D scanning, though it appears incremental as it builds on existing unsupervised methods.
The paper tackles the problem of partial point cloud registration where overlap between clouds may be small, by proposing an unsupervised network (OBMNet) with an Overlap Bias Matching Module to capture overlapping regions and predict bias coefficients. Experimental results show it achieves significant improvement over state-of-the-art methods, maintaining efficacy even at low overlap ratios.
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.