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.

我们提出了一种基于无监督网络的Overlap Bias Matching Network (OBMNet)用于局部点云配准，可有效解决无监督方法在重叠评估方面的不足，通过Overlap Bias Matching Module (OBMM)和neighbor map matching module的精准合并匹配得分等方法，可在低重叠比例的配准场景中保持有效性，实验证明该方法相比先前的配准方法有显著的提升。

点云配准需要重叠偏差匹配