Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111347
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Type: Conference paper
Title: "Maximizing rigidity" revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views
Author: Ji, P.
Li, H.
Dai, Y.
Reid, I.
Citation: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2017 / vol.2017-October, pp.929-937
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2017
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781538610336
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (ICCV 2017) (22 Oct 2017 - 29 Oct 2017 : Venice, ITALY)
Statement of
Responsibility: 
Pan Ji, Hongdong Li, Yuchao Dai, Ian Reid
Abstract: Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion (NRSfM) have long been treated in the literature as separate (different) problems. Inspired by a previous work which solved directly for 3D scene structure by factoring the relative camera poses out, we revisit the principle of “maximizing rigidity” in structure-from-motion literature, and develop a unified theory which is applicable to both rigid and non-rigid structure reconstruction in a rigidity-agnostic way. We formulate these problems as a convex semi-definite program, imposing constraints that seek to apply the principle of minimizing non-rigidity. Our results demonstrate the efficacy of the approach, with stateof- the-art accuracy on various 3D reconstruction problems.
Rights: © 2017 IEEE
RMID: 0030083146
DOI: 10.1109/ICCV.2017.106
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FL130100102
http://purl.org/au-research/grants/arc/DE140100180
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8234942
Appears in Collections:Computer Science publications

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