Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/105616
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Fast rotation search with stereographic projections for 3D registration
Author: Parra Bustos, A.
Chin, T.
Eriksson, A.
Li, H.
Suter, D.
Citation: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016; 38(11):2227-2240
Publisher: IEEE Computer Society
Issue Date: 2016
ISSN: 0162-8828
1939-3539
Statement of
Responsibility: 
Alvaro Parra Bustos, Tat-Jun Chin, Anders Eriksson, Hongdong Li and David Suter
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.
Keywords: Point cloud registration; rotation search; branch-and-bound; stereographic projections; R-trees; three-dimensional displays; optimization; transforms; iterative closest point algorithm; robustness; kernel; search problems
Rights: © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030066912
DOI: 10.1109/TPAMI.2016.2517636
Grant ID: http://purl.org/au-research/grants/arc/DP130102524
http://purl.org/au-research/grants/arc/DE130101775
http://purl.org/au-research/grants/arc/DP120103896
http://purl.org/au-research/grants/arc/DP130104567
http://purl.org/au-research/grants/arc/CE140100016
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
RA_hdl_105616.pdfRestricted Access1.44 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.