ALCReg: Active Label Correction for Partial Point Cloud Registration

Zongyi Xu, Xinqi Jiang, Xinyu Gao, Shanshan Zhao, Qianni Zhang, Weisheng Li, Xinbo Gao

Abstract: Deep point cloud registration methods encounter challenges due to partial overlaps and are heavily reliant on labeled data. In this paper, we propose ALCReg, an active label correction method for partial point cloud registration learning. ALCReg utilises a multimodal approach to generate pseudo labels, mitigating the cold-start issue in active learning. To ensure the diversity and representativeness of selected samples, we propose an inlier ratio based query strategy for manual correction. Furthermore, an innovative self-correction mechanism based on consistency is introduced, allowing the model to refine pseudo labels autonomously and further improve model performance. Experimental results on the 3DMatch and 3DLoMatch datasets demonstrate that ALCReg achieves comparable performance with the fully-supervised registration methods, even with only 5% of labeled samples, making it the first active learning method tailored for partial point cloud registration. Code is available at https://github.com/Jiang0903/ALCReg.

External IDs:dblp:conf/icmcs/XuJGZZLG25