Dense Isometric Non-Rigid Shape-From-Motion Based on Graph Optimization and Edge Selection

Chen, Y; Zhao, L; Zhang, Y; Huang, S

Dense Isometric Non-Rigid Shape-From-Motion Based on Graph Optimization and Edge Selection

Chen, Y

Zhao, L

Zhang, Y Huang, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2020, 5, (4), pp. 5889-5896
Issue Date:: 2020-10-01

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Field	Value	Language
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Zhang, Y
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.date.accessioned	2021-04-28T14:03:50Z
dc.date.available	2021-04-28T14:03:50Z
dc.date.issued	2020-10-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2020, 5, (4), pp. 5889-5896
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/148511
dc.description.abstract	In this letter, we propose a novel framework for dense isometric non-rigid shape-from-motion (Iso-NRSfM) based on graph topology and edge selection. A weighted undirected graph, of which nodes, edges, and weighted values are respectively the images, the image warps, and the number of the common features, is built. An edge selection algorithm based on maximum spanning tree and sub-modular optimization is presented to pick out the well-connected sub-graph for the warps with multiple images. Using the infinitesimal planarity assumption, the Iso-NRSfM problem is formulated as a graph optimization problem with the virtual measurements, which are based on metric tensor and Christoffel Symbol, and the variables related to the derivatives of the constructed points along the surface. The solution of this graph optimization problem directly leads to the normal field of the shape. Then, using a separable iterative optimization method, we obtain the dense point cloud with texture corresponding to the deformable shape robustly. In the experiments, the proposed method outperforms existing work in terms of constructed accuracy, especially when there exists missing/appearing (changing) data, noisy data, and outliers.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2020.3010199
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0913 Mechanical Engineering
dc.title	Dense Isometric Non-Rigid Shape-From-Motion Based on Graph Optimization and Edge Selection
dc.type	Journal Article
utslib.citation.volume	5
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-10-31T00:00:00+1000Z
dc.date.updated	2021-04-28T14:03:48Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	4

Abstract:

In this letter, we propose a novel framework for dense isometric non-rigid shape-from-motion (Iso-NRSfM) based on graph topology and edge selection. A weighted undirected graph, of which nodes, edges, and weighted values are respectively the images, the image warps, and the number of the common features, is built. An edge selection algorithm based on maximum spanning tree and sub-modular optimization is presented to pick out the well-connected sub-graph for the warps with multiple images. Using the infinitesimal planarity assumption, the Iso-NRSfM problem is formulated as a graph optimization problem with the virtual measurements, which are based on metric tensor and Christoffel Symbol, and the variables related to the derivatives of the constructed points along the surface. The solution of this graph optimization problem directly leads to the normal field of the shape. Then, using a separable iterative optimization method, we obtain the dense point cloud with texture corresponding to the deformable shape robustly. In the experiments, the proposed method outperforms existing work in terms of constructed accuracy, especially when there exists missing/appearing (changing) data, noisy data, and outliers.

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http://hdl.handle.net/10453/148511