Li, B.Shen, C.Dai, Y.Van Den Hengel, A.He, M.2017-09-112017-09-112015Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol.07-12-June-2015, pp.1119-112797814673696401063-6919http://hdl.handle.net/2440/107638Predicting 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 re- fined 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. Experi- ments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.en© 2015 IEEEConference paper003004629410.1109/CVPR.2015.72987150003879592010152-s2.0-84952783215239634Van Den Hengel, A. [0000-0003-3027-8364]