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.

翻译：点云配准是多个领域中的基础问题。实际场景中，待配准点云之间的重叠区域可能较小。大多数无监督方法缺乏对重叠区域的初始有效评估，导致配准精度欠佳。为解决此问题，我们提出一种用于部分重叠点云配准的无监督网络——重叠偏置匹配网络（OBMNet）。具体而言，我们设计了一个即插即用的重叠偏置匹配模块（OBMM），该模块包含两个核心组件：重叠采样模块和偏置预测模块。这两个组件分别用于捕获重叠区域的分布特征并预测点云共有结构的偏置系数。随后，我们将OBMM与邻域映射匹配模块集成，通过精确融合邻域内各点的匹配得分来鲁棒地识别对应关系，从而解决单点特征存在的歧义性问题。OBMNet在低重叠率的成对配准场景中仍能保持有效性。在多个数据集上的实验结果表明，与最先进的配准方法相比，本方法在性能上实现了显著提升。