Overlap Bias Matching is Necessary for Point Cloud Registration
Pengcheng Shi Jie Zhang Haozhe Cheng Junyang Wang Yiyang Zhou Chenlin Zhao Jihua Zhu
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.