As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each of which represents a consensus set. We perform node-guided clique selection then, where each node corresponds to the maximal clique with the greatest graph weight. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. Extensive experiments on U3M, 3DMatch, 3DLoMatch and KITTI demonstrate that MAC effectively increases registration accuracy, outperforms various state-of-the-art methods and boosts the performance of deep-learned methods. MAC combined with deep-learned methods achieves state-of-the-art registration recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch.

翻译：作为计算机视觉中的基础问题，三维点云配准旨在寻找最优位姿以对齐点云对。本文提出了一种基于最大团的3D配准方法。核心思想在于放宽先前的最大团约束，在图中挖掘更多局部一致性信息以生成精确位姿假设：1) 构建相容性图来表征初始对应点之间的亲和关系。2) 在图中搜索最大团，每个最大团代表一个一致性集合。随后执行节点引导的团选择，其中每个节点对应具有最大图权重的最大团。3) 通过SVD算法为选定的团计算变换假设，并采用最优假设进行配准。在U3M、3DMatch、3DLoMatch和KITTI上的大量实验表明，MAC有效提升了配准精度，优于多种最先进方法，并增强了深度学习方法的表现。MAC与深度学习方法结合后，在3DMatch/3DLoMatch上实现了95.7%/78.9%的最先进配准召回率。