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PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62076070.

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Correspondence to Xinrong Chen or Manning Wang .

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Yuan, M., Li, Z., Jin, Q., Chen, X., Wang, M. (2022). PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_35

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