R. Arandjelovi´carandjelovi´c and A. Zisserman, Smooth object retrieval using a bag of boundaries, International Conference on Computer Vision (ICCV), 2011.

M. Aubry, D. Maturana, A. A. Efros, B. C. Russell, and J. Sivic, Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.487

URL : https://hal.archives-ouvertes.fr/hal-01057240

R. Collobert, K. Kavukcuoglu, and C. Farabet, Torch7: A Matlab-like environment for machine learning, BigLearn, NIPS Workshop, 2011.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A largescale hierarchical image database, International Conference on Computer Vision and Pattern Recognition (CVPR), pp.248-255, 2009.

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, pp.303-338, 2010.
DOI : 10.1371/journal.pcbi.0040027

P. Felzenszwalb, R. Girshick, D. Mcallester, and D. Ramanan, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, p.32, 2010.
DOI : 10.1109/TPAMI.2009.167

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.81

URL : http://arxiv.org/pdf/1311.2524

D. Glasner, M. Galun, S. Alpert, R. Basri, and G. Shakhnarovich, Viewpoint-aware object detection and pose estimation, International Conference on Computer Vision (ICCV), 2011.
DOI : 10.1109/iccv.2011.6126379

URL : http://www.ai.mit.edu/people/gregory/papers/iccv2011.pdf

K. He, X. Zhang, S. Ren, and J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, European Conference on Computer Vision (ECCV), pp.346-361, 2014.

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), 2015.
DOI : 10.1109/CVPR.2016.90

URL : http://arxiv.org/pdf/1512.03385

M. Hejrati and D. Ramanan, Analyzing 3D objects in cluttered images, Advances in Neural Information Processing Systems (NIPS), 2012.

D. P. Huttenlocher and S. Ullman, Object recognition using alignment, International Conference on Computer Vision (ICCV), 1987.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.
DOI : 10.1162/neco.2009.10-08-881

Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989.
DOI : 10.1007/BF00133697

Y. Li, N. Snavely, D. Huttenlocher, and P. Fua, Worldwide pose estimation using 3D point clouds, European Conference on Computer Vision (ECCV), 2012.
DOI : 10.1007/978-3-642-33718-5_2

URL : https://infoscience.epfl.ch/record/201014/files/global_pose.pdf

J. J. Lim, H. Pirsiavash, and A. Torralba, Parsing IKEA Objects: Fine Pose Estimation, 2013 IEEE International Conference on Computer Vision, pp.2992-2999, 2013.
DOI : 10.1109/ICCV.2013.372

URL : http://people.csail.mit.edu/lim/paper/lpt_iccv2013.pdf

D. Lowe, The viewpoint consistency constraint, International Journal of Computer Vision, vol.171, issue.1, pp.57-72, 1987.
DOI : 10.1177/027836498400300301

F. Massa, M. Aubry, and R. Marlet, Convolutional neural networks for joint object detection and pose estimation: A comparative study, 2014.

M. Osadchy, Y. Lecun, and M. L. Miller, Synergistic Face Detection and Pose Estimation with Energy-Based Models, The Journal of Machine Learning Research (JMLR), vol.8, pp.1197-1215, 2007.
DOI : 10.1007/11957959_10

H. Penedones, R. Collobert, F. Fleuret, and D. Grangier, Improving object classification using pose information, 2011.

B. Pepik, M. Stark, P. Gehler, and B. Schiele, Teaching 3D geometry to deformable part models, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3362-3369, 2012.
DOI : 10.1109/CVPR.2012.6248075

URL : http://ps.is.tue.mpg.de/publications/59/get_file/

O. Pedro, R. Pinheiro, P. Collobert, and . Dollar, Learning to segment object candidates, Advances in Neural Information Processing Systems (NIPS), pp.1990-1998, 2015.

L. Roberts, Machine perception of 3-D solids, 1965.

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition. arXiv preprint, 2014.

H. Su, C. R. Qi, Y. Li, and L. J. Guibas, Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views, 2015 IEEE International Conference on Computer Vision (ICCV), pp.2686-2694, 2015.
DOI : 10.1109/ICCV.2015.308

S. Tulsiani and J. Malik, Viewpoints and keypoints, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1510-1519, 2015.
DOI : 10.1109/CVPR.2015.7298758

URL : http://arxiv.org/pdf/1411.6067

Y. Xiang, R. Mottaghi, and S. Savarese, Beyond PASCAL: A benchmark for 3D object detection in the wild, IEEE Winter Conference on Applications of Computer Vision, 2014.
DOI : 10.1109/WACV.2014.6836101

Y. Xiang and S. Savarese, Estimating the aspect layout of object categories, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248081

M. Zia, M. Stark, B. Schiele, and K. Schindler, Detailed 3D Representations for Object Recognition and Modeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.11, p.2013
DOI : 10.1109/TPAMI.2013.87