Depth and Surface Normal Estimation From Monocular Images Using Regression on Deep Features and Hierarchical CRFs

Bo Li, Chunhua Shen, Yuchao Dai, Anton van den Hengel, Mingyi He; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1119-1127

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 under-determined 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.

Related Material


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[bibtex]
@InProceedings{Li_2015_CVPR,
author = {Li, Bo and Shen, Chunhua and Dai, Yuchao and van den Hengel, Anton and He, Mingyi},
title = {Depth and Surface Normal Estimation From Monocular Images Using Regression on Deep Features and Hierarchical CRFs},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}