Abstract
Abstract
The majority of existing point cloud registration (PCR) methods exhibit performance degradation under extremely low inlier ratios. To address this limitation, this paper presents a robust learning-free estimator called MAC1to
K
, which relaxes the feature matching with maximal cliques from the following three perspectives: 1) A novel one-to-
K
feature matching rectification that iteratively rectifies false matches from their multiple candidates, which improves the overall quality of correspondences, increasing the inlier ratio by 13.76% and 17.70% for FPFH and FCGF descriptors, respectively, on 3DMatch. 2) A novel hypothesis generation method utilizing putative seeds through voting to guide the construction of maximal clique pools, effectively preserving more potential correct hypotheses. 3) A progressive hypothesis evaluation method that continuously reduces the solution space with a “global-clusters-cluster-individual” manner rather than traditional one-shot techniques, greatly alleviating the issue of missing good hypotheses. Unlike MAC, MAC1to
K
exhibits a capacity to process data with an extremely low inlier ratio. For instance, it achieved 28.59% and 33.96% improvements in registration recall on 3DMatch and 3DLoMatch, respectively, with fewer than 1% inliers. Therefore, MAC1to
K
demonstrated the state-of-the-art performance.