报告人：Tat-Jun Chin

时间：8月18日周一 上午10:00

地点：海韵行政楼C505

题目：Fast Rotation Search with Stereographic Projections for 3D Registration

摘要: 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 the rigid transform, which are usually harder to optimise than the translational 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. Our bound leads to more aggresive pruning of the search space and speeds up BnB tremendously. 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.

个人简历: Tat-Jun Chin obtained his PhD in Computer Systems Engineering from Monash University, Australia, in 2007. He was supported by an Endeavour Australia-Asia Award 2004. He was a Research Fellow at the Institute for Infocomm Research (I2R) in Singapore from 2007- 2008. From 2008 onwards, he was Postdoc, Lecturer, then Senior Lecturer at the University of Adelaide, Australia. Dr Chin's main research interest is computer vision, in particular geometric optimisation, parameter estimation and statistical learning methods.