  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.
