The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially overlapping scenarios. In this paper, we propose an effective inlier estimation method for unsupervised point cloud registration by capturing geometric structure consistency between the source point cloud and its corresponding reference point cloud copy. Specifically, to obtain a high quality reference point cloud copy, an One-Nearest Neighborhood (1-NN) point cloud is generated by input point cloud. This facilitates matching map construction and allows for integrating dual neighborhood matching scores of 1-NN point cloud and input point cloud to improve matching confidence. Benefiting from the high quality reference copy, we argue that the neighborhood graph formed by inlier and its neighborhood should have consistency between source point cloud and its corresponding reference copy. Based on this observation, we construct transformation-invariant geometric structure representations and capture geometric structure consistency to score the inlier confidence for estimated correspondences between source point cloud and its reference copy. This strategy can simultaneously provide the reliable self-supervised signal for model optimization. Finally, we further calculate transformation estimation by the weighted SVD algorithm with the estimated correspondences and corresponding inlier confidence. We train the proposed model in an unsupervised manner, and extensive experiments on synthetic and real-world datasets illustrate the effectiveness of the proposed method.

翻译：无监督点云配准方法的精度通常受限于缺乏可靠的离群点估计和自监督信号，尤其在部分重叠场景下。本文提出一种有效的离群点估计方法，通过捕捉源点云与其对应参考点云副本之间的几何结构一致性，实现无监督点云配准。具体而言，为获取高质量的参考点云副本，我们由输入点云生成一种最近邻域（1-NN）点云。这有助于构建匹配映射，并允许整合1-NN点云与输入点云的双重邻域匹配分数以提升匹配置信度。得益于高质量参考副本，我们论证：由内点及其邻域构成的邻域图在源点云与其对应参考副本之间应具有一致性。基于这一观察，我们构建变换不变的几何结构表示，并捕捉几何结构一致性，从而为源点云与其参考副本之间的估计对应关系评分内点置信度。该策略可同时为模型优化提供可靠的自监督信号。最终，我们利用加权SVD算法，结合估计对应关系及对应的内点置信度，进一步计算变换估计。我们以无监督方式训练所提出模型，在合成数据集与真实数据集上的广泛实验验证了该方法的有效性。