Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud. Solving this problem is challenging since inlier correspondences of one instance constitute outliers of all the other instances. Existing methods often rely on time-consuming hypothesis sampling or features leveraging spatial consistency, resulting in limited performance. In this paper, we propose PointCLM, a contrastive learning-based framework for mutli-instance point cloud registration. We first utilize contrastive learning to learn well-distributed deep representations for the input putative correspondences. Then based on these representations, we propose a outlier pruning strategy and a clustering strategy to efficiently remove outliers and assign the remaining correspondences to correct instances. Our method outperforms the state-of-the-art methods on both synthetic and real datasets by a large margin. The code will be made publicly available at http://github.com/phdymz/PointCLM.
CITATION STYLE
Yuan, M., Li, Z., Jin, Q., Chen, X., & Wang, M. (2022). PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13669 LNCS, pp. 595–611). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20077-9_35
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