Efficient and Accurate Multi-Instance Point Cloud Registration with Iterative Main Cluster Detection

Abstract

Multi-instance point cloud registration is the problem of recovering the poses of all instances of a model point cloud in a scene point cloud. A traditional solution first extracts correspondences and then clusters the correspondences into different instances. We propose an efficient and robust method which clusters the correspondences in an iterative manner. In each iteration, our method first computes the spatial compatibility matrix between the correspondences, and detects its main cluster. The main cluster indicates a potential occurrence of an instance, and we estimate the pose of this instance with the correspondences in the main cluster. Afterwards, the correspondences are removed to further register new instances in the following iterations. With this simplistic design, our method can adaptively determine the number of instances, achieving significant improvements on both efficiency and accuracy.