HybridPoint：基于混合点采样与匹配的点云配准
HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching
AI总结：
针对点云配准中超点定位精度差导致块划分模糊的问题，提出基于混合点（显著点与均匀点）采样与双类块匹配的HybridPoint网络，在多个基准数据集上实现了最先进的配准性能。
摘要：
块到点匹配已成为点云配准的一种稳健方法。然而，以往的块匹配方法采用定位精度较差的超点作为节点，这可能导致模糊的块划分。在本文中，我们提出了一种基于HybridPoint的网络，以寻找更稳健和准确的对应关系。首先，我们提出使用具有显著局部特征的显著点作为节点来增加块重复性，并引入一些均匀分布的点来补全点云，从而构成混合点。混合点不仅具有更好的定位精度，而且能给出整个点云的完整图景。此外，基于混合点的特性，我们提出了一个双类块匹配模块，该模块利用显著点的匹配结果并过滤非显著点的匹配噪声。实验表明，我们的模型在3DMatch、3DLoMatch和KITTI里程计上取得了最先进的性能，特别是在3DMatch数据集上达到了93.0%的配准召回率。我们的代码和模型可在https://github.com/liyih/HybridPoint获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
