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

Xiyu Zhang, Yanning Zhang, Jiaqi Yang

Keywords: Hypothesis generation, Hypothesis evaluation, Maximal clique, 3D registration

TL;DR: MAC++ is a robust, learning-free estimator for 3D point cloud registration in challenging scenarios with low inlier ratios.

Abstract: Maximal cliques (MAC) represent a novel state-of-the-art 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 ``global-clusters-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). The code will be released.

Submission Number: 3