Abstract

Registering two 3D point clouds involves estimating the rigid transform that brings the two point clouds into alignment. Recently there has been a surge of interest in using branch-and-bound (BnB) optimisation for point cloud registration. While BnB guarantees globally optimal solutions, it is usually too slow to be practical. A fundamental source of difficulty lies in the search for the rotational parameters. In this work, first by assuming that the translation is known, we focus on constructing a fast rotation search algorithm. With respect to an inherently robust geometric matching criterion, we propose a novel bounding function for BnB that is provably tighter than previously proposed bounds. Further, we also propose a fast algorithm to evaluate our bounding function. Our idea is based on using stereographic projections to precompute and index all possible point matches in spatial R-trees for rapid evaluations. The result is a fast and globally optimal rotation search algorithm. To conduct full 3D registration, we co-optimise the translation by embedding our rotation search kernel in a nested BnB algorithm. Since the inner rotation search is very efficient, the overall 6DOF optimisation is speeded up significantly without losing global optimality. On various challenging point clouds, including those taken out of lab settings, our approach demonstrates superior efficiency.