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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)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13669))

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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.

Author information

Authors and Affiliations

  1. Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China

    Mingzhi Yuan, Zhihao Li, Qiuye Jin, Xinrong Chen & Manning Wang

  2. Academy for Engineering and Technology, Fudan University, Shanghai, 200032, China

    Xinrong Chen

  3. Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China

    Mingzhi Yuan, Zhihao Li, Qiuye Jin, Xinrong Chen & Manning Wang

Authors
  1. Mingzhi Yuan
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  2. Zhihao Li
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  3. Qiuye Jin
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  4. Xinrong Chen
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  5. Manning Wang
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Corresponding authors

Correspondence to Xinrong Chen or Manning Wang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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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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  • DOI: https://doi.org/10.1007/978-3-031-20077-9_35

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