PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration. (arXiv:2209.00219v1 [cs.CV])

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 …

arxiv cloud framework learning registration