@article{Zhang2026, 
author = {Xiyu Zhang and Zhiyi Xia and Zhengbao Wang and Jiwei Deng and Siwen Quan and Qingshan Xu and Jiaqi Yang},
title = {MAC1toK: relax feature matching with maximal cliques for 3D registration},
year = {2026},
journal = {Visual Intelligence},
volume = {4},
pages = {24},
keywords = {Point cloud registration, Correspondence, Relax constraint, Rigid registration},
url = {https://www.sciopen.com/article/10.1007/s44267-026-00124-2},
doi = {10.1007/s44267-026-00124-2},
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 MAC1toK, 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, MAC1toK 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, MAC1toK demonstrated the state-of-the-art performance.}
}