Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes, which may lead to ambiguous patch partitions. In this paper, we propose a HybridPoint-based network to find more robust and accurate correspondences. Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points. Hybrid points not only have better localization precision but also give a complete picture of the whole point cloud. Furthermore, based on the characteristic of hybrid points, we propose a dual-classes patch matching module, which leverages the matching results of salient points and filters the matching noise of non-salient points. Experiments show that our model achieves state-of-the-art performance on 3DMatch, 3DLoMatch, and KITTI odometry, especially with 93.0% Registration Recall on the 3DMatch dataset. Our code and models are available at https://github.com/liyih/HybridPoint.

翻译：块到点匹配已成为点云配准中鲁棒性较强的技术途径。然而，现有块匹配方法采用定位精度较差的超级点作为节点，可能导致块划分模糊。本文提出基于混合点的网络模型，以获取更鲁棒且更精确的对应关系。首先，我们采用具有显著局部特征的显著点作为节点以提高块可重复性，并引入均匀分布点以完善点云结构，从而构成混合点。混合点不仅具有更优的定位精度，更能全面表征点云整体结构。其次，基于混合点的特性，我们提出双类别块匹配模块，该模块利用显著点的匹配结果，同时滤除非显著点的匹配噪声。实验表明，本模型在3DMatch、3DLoMatch和KITTI里程计数据集上均达到最先进性能，尤其在3DMatch数据集上实现了93.0%的配准召回率。相关代码与模型已开源于https://github.com/liyih/HybridPoint。