NDLI: Dense Depth Map Reconstruction from Sparse Measurements Using a Multilayer Conditional Random Field Model

Content Provider	IEEE Xplore Digital Library
Author	Li, F. Li, E. Shafiee, M.J. Wong, A. Zelek, J.
Copyright Year	2015
Description	Author affiliation: Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada (Li, F.; Li, E.; Shafiee, M.J.; Wong, A.; Zelek, J.)
Abstract	Acquiring accurate dense depth maps is crucial for accurate 3D reconstruction. Current high quality depth sensors capable of generating dense depth maps are expensive and bulky, while compact low-cost sensors can only reliably generate sparse depth measurements. We propose a novel multilayer conditional random field (MCRF) approach to reconstruct a dense depth map of a target scene given the sparse depth measurements and corresponding photographic measurements obtained from stereo photogrammetric systems. Estimating the dense depth map is formulated as a maximum posterior (MAP) inference problem where a smoothness prior is assumed. Our MCRF model uses the sparse depth measurement as an additional observation layer and describes relations between nodes with multivariate feature functions based on the depth and photographic measurements. The method is first qualitatively analyzed when performed on data collected with a compact stereo camera, then quantitative performance is measured using the Middlebury stereo vision data for ground truth. Experimental results show our method performs well for reconstructing simple scenes and has lower mean squared error compared to other dense depth map reconstruction methods.
Starting Page	86
Ending Page	93
File Size	3888714
Page Count	8
File Format	PDF
ISBN	9781479919864
DOI	10.1109/CRV.2015.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-03
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multilayer conditional random field Three-dimensional displays Current measurement Reconstruction algorithms depth map Cameras Nonhomogeneous media 3D visualisation Sensors Image reconstruction Stereophotogrammetry
Content Type	Text
Resource Type	Article

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