Real Time Complete Dense Depth Reconstruction for a Monocular Camera

Huang, X; Fan, L; Zhang, J; Wu, Q; Yuan, C

Real Time Complete Dense Depth Reconstruction for a Monocular Camera

Huang, X

Fan, L Zhang, J

Wu, Q

Yuan, C

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Publication Type:: Conference Proceeding
Citation:: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2016, pp. 674 - 679
Issue Date:: 2016-12-16

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Field	Value	Language
dc.contributor.author	Huang, X https://orcid.org/0000-0002-3579-538X	en_US
dc.contributor.author	Fan, L	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Yuan, C	en_US
dc.date.issued	2016-12-16	en_US
dc.identifier.citation	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2016, pp. 674 - 679	en_US
dc.identifier.isbn	9781467388504	en_US
dc.identifier.issn	2160-7508	en_US
dc.identifier.uri	http://hdl.handle.net/10453/95571
dc.description.abstract	© 2016 IEEE. In this paper, we aim to solve the problem of estimating complete dense depth maps from a monocular moving camera. By 'complete', we mean depth information is estimated for every pixel and detailed reconstruction is achieved. Although this problem has previously been attempted, the accuracy of complete dense depth reconstruction is a remaining problem. We propose a novel system which produces accurate complete dense depth map. The new system consists of two subsystems running in separated threads, namely, dense mapping and sparse patch-based tracking. For dense mapping, a new projection error computation method is proposed to enhance the gradient component in estimated depth maps. For tracking, a new sparse patch-based tracking method estimates camera pose by minimizing a normalized error term. The experiments demonstrate that the proposed method obtains improved performance in terms of completeness and accuracy compared to three state-of the-art dense reconstruction methods VSFM+CMVC, LSDSLAM and REMODE.	en_US
dc.relation.ispartof	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops	en_US
dc.relation.isbasedon	10.1109/CVPRW.2016.89	en_US
dc.title	Real Time Complete Dense Depth Reconstruction for a Monocular Camera	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In this paper, we aim to solve the problem of estimating complete dense depth maps from a monocular moving camera. By 'complete', we mean depth information is estimated for every pixel and detailed reconstruction is achieved. Although this problem has previously been attempted, the accuracy of complete dense depth reconstruction is a remaining problem. We propose a novel system which produces accurate complete dense depth map. The new system consists of two subsystems running in separated threads, namely, dense mapping and sparse patch-based tracking. For dense mapping, a new projection error computation method is proposed to enhance the gradient component in estimated depth maps. For tracking, a new sparse patch-based tracking method estimates camera pose by minimizing a normalized error term. The experiments demonstrate that the proposed method obtains improved performance in terms of completeness and accuracy compared to three state-of the-art dense reconstruction methods VSFM+CMVC, LSDSLAM and REMODE.

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http://hdl.handle.net/10453/95571