MAC++: Going Further with Maximal Cliques for 3D Registration

Maximal cliques (MAC) represent a novel state-of-theart approach for 3D registration from correspondences, however, it still suffers from extremely severe outliers. In this paper, we introduce a robust learning-free estimator called MAC++, exploring maximal cliques for 3D registration from the following two perspectives: 1) A novel hypothesis generation method utilizing putative seeds through voting to guide the construction of maximal clique pools, effectively preserving more potential correct hypotheses. 2) A progressive hypothesis evaluation method that continuously reduces the solution space in a globalclusters-cluster-individual manner rather than traditional one-shot techniques, greatly alleviating the issue of missing good hypotheses. Experiments conducted on U3M, 3DMatch/3DLoMatch, and KITTI-LC datasets show the new state-of-the-art performance of MAC++. MAC++ demonstrates the capability to handle extremely low inlier ratio data where MAC fails (e.g., showing 27.1%/30.6% registration recall improvements on 3DMatch/3DLoMatch with < 1% inliers).