Improved Techniques for Multi-view Registration with Motion Averaging

Abstract

Recently, motion averaging has been introduced as an effective means to solve multi-view registration problem. This approach utilizes the Lie-algebras to implement the averaging of many relative motions, each of which corresponds to the registration result of the scan pair involved in multi-view registration. Accordingly, a key question is how to obtain accurate registration between two partially overlapping scans. This paper presents a method to estimate the overlapping percentage between each scan pair involved in multi-view registration. What's more, it applies the trimmed iterative closest point (TrICP) algorithm to obtain accurate relative motions for the scan pairs including high overlapping percentage. Besides, it introduces the parallel computation to increase the efficiency of multi-view registration. Experimental results carried out with public data sets illustrate its superiority over previous approaches.