  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
