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

• URL: http://arxiv.org/abs/2209.00219v1
• Date: Thu, 1 Sep 2022 04:30:05 GMT
• Title: PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration
• Authors: Mingzhi Yuan, Zhihao Li, Qiuye Jin, Xinrong Chen, Manning Wang
• Abstract summary: PointCLM is a contrastive learning-based framework for mutli-instance point cloud registration. Our method outperforms the state-of-the-art methods on both synthetic and real datasets by a large margin.
• Score: 4.969636478156443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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