Stereo Matching via Dual Fusion

Dong, H; Wang, T; Yu, X; Ren, P

Stereo Matching via Dual Fusion

Dong, H Wang, T Yu, X

Ren, P

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2018, 25 (5), pp. 615 - 619
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Dong, H	en_US
dc.contributor.author	Wang, T	en_US
dc.contributor.author	Yu, X https://orcid.org/0000-0002-8941-2698	en_US
dc.contributor.author	Ren, P	en_US
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	IEEE Signal Processing Letters, 2018, 25 (5), pp. 615 - 619	en_US
dc.identifier.issn	1070-9908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132700
dc.description.abstract	© 1994-2012 IEEE. We present a two-fold fusion framework that constructs comprehensive cost volumes for stereo matching. To this end, we develop fusion schemes at two key steps, i.e., a) the raw cost computation and b) the cost aggregation. Specifically, we commence by fusing both structure- and data-oriented features as raw costs. We then incorporate the guided filtered costs into the cross-based cost aggregation and obtain the fused aggregated costs. The fusion schemes at the two steps effectively complement each other and result in an accurate disparity map. Experiments on the Middlebury benchmark v3 demonstrate the state-of-the-art performance of our framework in terms of various metrics.	en_US
dc.relation.ispartof	IEEE Signal Processing Letters	en_US
dc.relation.isbasedon	10.1109/LSP.2018.2816905	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Stereo Matching via Dual Fusion	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	25	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
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/Students
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 1994-2012 IEEE. We present a two-fold fusion framework that constructs comprehensive cost volumes for stereo matching. To this end, we develop fusion schemes at two key steps, i.e., a) the raw cost computation and b) the cost aggregation. Specifically, we commence by fusing both structure- and data-oriented features as raw costs. We then incorporate the guided filtered costs into the cross-based cost aggregation and obtain the fused aggregated costs. The fusion schemes at the two steps effectively complement each other and result in an accurate disparity map. Experiments on the Middlebury benchmark v3 demonstrate the state-of-the-art performance of our framework in terms of various metrics.

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http://hdl.handle.net/10453/132700