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

2022-09-01 04:30:05
Mingzhi Yuan, Zhihao Li, Qiuye Jin, Xinrong Chen, Manning Wang

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.

Abstract (translated)

URL

https://arxiv.org/abs/2209.00219

PDF

https://arxiv.org/pdf/2209.00219.pdf


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