M. H. Baig and L. Torresani, Coupled depth learning, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
DOI : 10.1109/WACV.2016.7477699

A. Chakrabarti, J. Shao, and G. Shakhnarovich, Depth from a single image by harmonizing overcomplete local network predictions, NIPS, 2008.

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, Semantic image segmentation with deep convolutional nets and fully connected crfs, ICLR, 2015, p.3
DOI : 10.1109/tpami.2017.2699184
URL : http://arxiv.org/pdf/1606.00915

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.4, 2016.
DOI : 10.1109/TPAMI.2017.2699184
URL : http://arxiv.org/pdf/1606.00915

W. Chen, Z. Fu, D. Yang, and J. Deng, Single-image depth perception in the wild, NIPS, 2016.

S. Choi, D. Min, B. Ham, Y. Kim, C. Oh et al., Depth Analogy: Data-Driven Approach for Single Image Depth Estimation Using Gradient Samples, IEEE Transactions on Image Processing, vol.24, issue.12, pp.5953-5966, 2015.
DOI : 10.1109/TIP.2015.2495261

K. Crammer and Y. Singer, Pranking with ranking, NIPS, 2002.

D. Eigen and R. Fergus, Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture, 2015 IEEE International Conference on Computer Vision (ICCV), 2008.
DOI : 10.1109/ICCV.2015.304

D. Eigen, C. Puhrsch, and R. Fergus, Depth map prediction from a single image using a multi-scale deep network, NIPS, 2008.

J. Flynn, I. Neulander, J. Philbin, and N. Snavely, Deepstereo: Learning to predict new views from the world's imagery, CVPR, 2016.

D. Forsyth and J. Ponce, Computer Vision: a Modern Approach, 2002.
URL : https://hal.archives-ouvertes.fr/hal-01063327

E. Frank and M. Hall, A Simple Approach to Ordinal Classification, 2001.
DOI : 10.1007/3-540-44795-4_13

R. Furukawa, R. Sagawa, and H. Kawasaki, Depth Estimation Using Structured Light Flow ??? Analysis of Projected Pattern Flow on an Object???s Surface, 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
DOI : 10.1109/ICCV.2017.497

R. Garg, G. Carneiro, and I. Reid, Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue, ECCV, 2016
DOI : 10.1109/ICCV.2015.179

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, vol.32, issue.11, 2006.
DOI : 10.1109/ICRA.2012.6225282

C. Godard, O. M. Aodha, and G. J. Brostow, Unsupervised Monocular Depth Estimation with Left-Right Consistency, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.699
URL : http://arxiv.org/pdf/1609.03677

R. A. Güler, G. Trigeorgis, E. Antonakos, P. Snape, S. Zafeiriou et al., DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2017.280

H. Ha, S. Im, J. Park, H. Jeon, and I. S. Kweon, Highquality depth from uncalibrated small motion clip, CVPR, 2016.
DOI : 10.1109/cvpr.2016.584

C. Hane, L. Ladicky, and M. Pollefeys, Direction matters: Depth estimation with a surface normal classifier, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298635

F. E. Harrell-jr, Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.5
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/pdf/1512.03385

R. Herbrich, T. Graepel, and K. Obermayer, Support vector learning for ordinal regression, 9th International Conference on Artificial Neural Networks: ICANN '99, 1999.
DOI : 10.1049/cp:19991091
URL : http://stat.cs.tu-berlin.de/publications/papers/hergraeober99b.ps.gz

D. Hoiem, A. A. Efros, and M. Hebert, Recovering Surface Layout from an Image, International Journal of Computer Vision, vol.63, issue.2, pp.151-172, 2007.
DOI : 10.3758/BF03211873
URL : http://www-2.cs.cmu.edu/%7Edhoiem/publications/hoiem_ijcv2007SurfaceLayout.pdf

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1145/2647868.2654889

K. Karsch, C. Liu, and S. B. Kang, Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.11, pp.2144-2158, 2014.
DOI : 10.1109/TPAMI.2014.2316835
URL : http://kevinkarsch.com/publications/tpami14-depthtransfer.pdf

A. Kendall and Y. Gal, What uncertainties do we need in bayesian deep learning for computer vision, NIPS, 2017.

S. Kim, K. Park, K. Sohn, and S. Lin, Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields, 2008.
DOI : 10.1109/CVPR.2014.97
URL : http://arxiv.org/pdf/1603.06359

N. Kong and M. J. Black, Intrinsic Depth: Improving Depth Transfer with Intrinsic Images, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.401

J. Konrad, M. Wang, P. Ishwar, C. Wu, and D. Mukherjee, Learning-Based, Automatic 2D-to-3D Image and Video Conversion, IEEE Transactions on Image Processing, vol.22, issue.9, pp.3485-3496, 2013.
DOI : 10.1109/TIP.2013.2270375

Y. Kuznietsov, J. Stückler, and B. Leibe, Semi-Supervised Deep Learning for Monocular Depth Map Prediction, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
DOI : 10.1109/CVPR.2017.238
URL : http://arxiv.org/pdf/1702.02706

L. Ladicky, J. Shi, and M. Pollefeys, Pulling Things out of Perspective, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2006.
DOI : 10.1109/CVPR.2014.19

I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, Deeper Depth Prediction with Fully Convolutional Residual Networks, 2016 Fourth International Conference on 3D Vision (3DV), 2006.
DOI : 10.1109/3DV.2016.32

B. Li, C. Shen, Y. Dai, A. Van-den-hengel, and M. He, Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs, CVPR, 2015.

J. Li, R. Klein, and A. Yao, A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images, 2017 IEEE International Conference on Computer Vision (ICCV)
DOI : 10.1109/ICCV.2017.365

X. Li, H. Qin, Y. Wang, Y. Zhang, and Q. Dai, DEPT: Depth Estimation by Parameter Transfer for Single Still Images, ACCV, 2014.
DOI : 10.1007/978-3-319-16808-1_4

B. Liu, S. Gould, and D. Koller, Single image depth estimation from predicted semantic labels, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539823
URL : http://ai.stanford.edu/%7Ekoller/Papers/Liu%2Bal%3ACVPR10.pdf

F. Liu, C. Shen, G. Lin, and I. Reid, Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.10, pp.2024-2039, 2006.
DOI : 10.1109/TPAMI.2015.2505283

M. Liu, M. Salzmann, and X. He, Discrete-Continuous Depth Estimation from a Single Image, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.97
URL : http://www.nicta.com.au/__data/assets/pdf_file/0005/40586/LiuSalzmannHeCVPR14.pdf

T. Narihira, M. Maire, and S. X. Yu, Learning lightness from human judgement on relative reflectance, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298915

P. K. Nathan-silberman, D. Hoiem, and R. Fergus, Indoor Segmentation and Support Inference from RGBD Images, ECCV, 2007.
DOI : 10.1007/978-3-642-33715-4_54

Z. Niu, M. Zhou, L. Wang, X. Gao, and G. Hua, Ordinal Regression with Multiple Output CNN for Age Estimation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.532

A. Oliva and A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, International Journal of Computer Vision, vol.42, issue.3, pp.145-175, 2001.
DOI : 10.1023/A:1011139631724

A. Rajagopalan, S. Chaudhuri, and U. Mudenagudi, Depth estimation and image restoration using defocused stereo pairs, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1521-1525, 2004.
DOI : 10.1109/TPAMI.2004.102

R. Ranftl, V. Vineet, Q. Chen, and V. Koltun, Dense Monocular Depth Estimation in Complex Dynamic Scenes, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.440

A. Roy and S. Todorovic, Monocular Depth Estimation Using Neural Regression Forest, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI : 10.1109/CVPR.2016.594

A. Saxena, S. H. Chung, and A. Y. Ng, Learning depth from single monocular images, NIPS, 2006.

A. Saxena, M. Sun, and A. Y. Ng, Make3D: Learning 3D Scene Structure from a Single Still Image, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.5, pp.824-840, 2006.
DOI : 10.1109/TPAMI.2008.132

D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), pp.7-42, 2002.
DOI : 10.1109/SMBV.2001.988771

A. Shashua and A. Levin, Ranking with large margin principle: Two approaches, NIPS, 2003.

E. Shelhamer, J. T. Barron, and T. Darrell, Scene Intrinsics and Depth from a Single Image, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015.
DOI : 10.1109/ICCVW.2015.39

J. Shi, X. Tao, L. Xu, and J. Jia, Break Ames room illusion, ACM Transactions on Graphics, vol.34, issue.6, p.225, 2015.
DOI : 10.1109/ICCPHOT.2009.5559018

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2005.

P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price et al., Towards unified depth and semantic prediction from a single image, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
DOI : 10.1109/CVPR.2015.7298897

X. Wang, D. Fouhey, and A. Gupta, Designing deep networks for surface normal estimation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298652

J. Xie, R. Girshick, and A. Farhadi, Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks, 2006.
DOI : 10.1109/TPAMI.2007.1166
URL : http://arxiv.org/pdf/1604.03650

D. Xu, E. Ricci, W. Ouyang, X. Wang, and N. Sebe, Multiscale continuous crfs as sequential deep networks for monocular depth estimation, CVPR, 2017.
DOI : 10.1109/cvpr.2017.25
URL : http://arxiv.org/pdf/1704.02157

X. You, Q. Li, D. Tao, W. Ou, and M. Gong, Local metric learning for exemplar-based object detection, IEEE TCSVT, vol.24, issue.8 2, pp.1265-1276, 2014.

F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions, 2003.

Z. Zhang, A. G. Schwing, S. Fidler, and R. Urtasun, Monocular Object Instance Segmentation and Depth Ordering with CNNs, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.300

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, Pyramid Scene Parsing Network, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.3
DOI : 10.1109/CVPR.2017.660
URL : http://arxiv.org/pdf/1612.01105

W. Zhuo, M. Salzmann, X. He, and M. Liu, Indoor scene structure analysis for single image depth estimation, CVPR, 2015.

D. Zoran, P. Isola, D. Krishnan, and W. T. Freeman, Learning Ordinal Relationships for Mid-Level Vision, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.52