  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.
