Multi-instance Point Cloud Registration by Efficient Correspondence Clustering
This addresses the challenge of robust and efficient multi-instance point cloud registration for applications like robotics and computer vision, representing a strong specific gain rather than incremental.
The paper tackles the problem of registering multiple instances of a source point cloud in a target point cloud, achieving an F1 score of 90.46% for up to 20 instances with 70% outliers and being at least 10 times faster than existing methods.
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