NDLI: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs

Content Provider	IEEE Xplore Digital Library
Author	Bo Li Chunhua Shen Yuchao Dai van den Hengel, A. Mingyi He
Copyright Year	2015
Description	Author affiliation: Univ. of Adelaide, Adelaide, SA, Australia (Chunhua Shen; van den Hengel, A.) \|\| Australian Nat. Univ., Canberra, ACT, Australia (Yuchao Dai) \|\| Northwestern Polytech. Univ., Xi'an, China (Bo Li; Mingyi He)
Abstract	Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task. This paper tackles this challenging and essentially underdetermined problem by regression on deep convolutional neural network (DCNN) features, combined with a post-processing refining step using conditional random fields (CRF). Our framework works at two levels, super-pixel level and pixel level. First, we design a DCNN model to learn the mapping from multi-scale image patches to depth or surface normal values at the super-pixel level. Second, the estimated super-pixel depth or surface normal is refined to the pixel level by exploiting various potentials on the depth or surface normal map, which includes a data term, a smoothness term among super-pixels and an auto-regression term characterizing the local structure of the estimation map. The inference problem can be efficiently solved because it admits a closed-form solution. Experiments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.
Starting Page	1119
Ending Page	1127
File Size	1790696
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298715
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Feature extraction Training Context Color Three-dimensional displays Training data
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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