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https://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 / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 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 |
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: | Aurora harvest 3 Computer Science publications |
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