We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to detect possible instances and reject the outliers, whose robustness and efficiency degrade notably when the number of instances and outliers increase. We propose to directly group the set of noisy correspondences into different clusters based on a distance invariance matrix. The instances and outliers are automatically identified through clustering. Our method is robust and fast. We evaluated our method on both synthetic and real-world datasets. The results show that our approach can correctly register up to 20 instances with an F1 score of 90.46% in the presence of 70% outliers, which performs significantly better and at least 10x faster than existing methods

翻译：我们解决了在目标点云中估计源点云层多重情况构成的问题。 现有的解决方案要求对大量假设进行取样,以发现可能的事例,并拒绝外部线,其稳健性和效率会降低,当事件和外部线数量增加时,这种情况明显会降低。 我们提议根据距离差异矩阵,将一组吵闹的通信直接分组到不同的组群中。 实例和外部线会通过集群自动识别。 我们的方法既健全又快速。 我们在合成和真实世界数据集中评估了我们的方法。 结果显示,我们的方法可以正确地登记到20个例子,在70%的外部线上,F1得分90.46%,比现有方法要好得多,至少10x快。