; Venu Madhav Govindu is with the Department of Electrical Engineering, Indian Institute of Science, Bengaluru, INDIA. Pooja A. is with Amazon India. (email: firstname.lastname@example.org)
In this paper we present an extension of the Iterative Closest Point (ICP) algorithm that simultaneously registers multiple 3D scans. While ICP fails to utilise the multiview constraints available in a set of scans, our method exploits the information redundancy in a set of 3D scans by using the averaging of relative motions. This averaging method utilises the Lie group structure of motions, resulting in a 3D registration method that is both efficient and accurate. In addition, we present two variants of our approach, i.e. a method that solves for multiview 3D registration while obeying causality and a transitive correspondence variant that efficiently solves the correspondence problem across multiple scans. We present experimental results to characterise our method and explain its behaviour as well as those of some other multiview registration methods in the literature. We establish the superior accuracy of our method in comparison with these multiview methods with registration results on a set of well-known real datasets of 3D scans.