G. J. Agin, Computer vision systems for industrial inspection and assembly, Computer, issue.5, pp.11-20, 1980.

J. Böhm, C. Brenner, J. Gühring, and D. Fritsch, Automated extraction of features from cad models for 3d object recognition, ISPRS Congress, 2000.

B. Chu, V. Madhavan, O. Beijbom, J. Hoffman, D. et al., Best practices for fine-tuning visual classifiers to new domains, ECCV, pp.435-442, 2016.

D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari et al., Scaling egocentric vision: The epic-kitchens dataset, ECCV, pp.720-736, 2018.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet: A large-scale hierarchical image database, CVPR, pp.248-255, 2009.

G. Evans, J. Miller, M. I. Pena, A. Macallister, and E. Winer, Evaluating the microsoft hololens through an augmented reality assembly application, Degraded Environments: Sensing, Processing, and Display, vol.10197, 2017.

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The pascal visual object classes (voc) challenge, IJCV, vol.88, issue.2, pp.303-338, 2010.

N. Gavish, T. Gutiérrez, S. Webel, J. Rodríguez, M. Peveri et al., Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks, Interactive Learning Environments, vol.23, issue.6, pp.778-798, 2015.

S. Hinterstoisser, S. Benhimane, V. Lepetit, P. Fua, and N. Navab, Simultaneous recognition and homography extraction of local patches with a simple linear classifier, BMVC, 2008.

S. Hinterstoisser, V. Lepetit, P. Wohlhart, and K. Konolige, On pre-trained image features and synthetic images for deep learning, ECCV Workshops, 2018.

T. Inoue, S. Choudhury, G. De-magistris, and S. Dasgupta, Transfer learning from synthetic to real images using variational autoencoders for precise position detection, IEEE ICIP, pp.2725-2729, 2018.

W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab, Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again, ICCV, pp.1521-1529, 2017.

G. Klein and D. Murray, Parallel tracking and mapping for small AR workspaces, IEEE/ACM International Symposium on Mixed and Augmented Reality (ISMAR), 2007.

G. Klein and D. Murray, Simulating low-cost cameras for augmented reality compositing, IEEE Transactions on Visualization and Computer Graphics, vol.16, issue.3, pp.369-380, 2009.

J. Langlois, H. Mouchère, N. Normand, and C. Viard-gaudin, 3d orientation estimation of industrial parts from 2d images using neural networks, In ICPRAM, pp.409-416, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01681124

E. N. Malamas, E. G. Petrakis, M. Zervakis, L. Petit, and J. Legat, A survey on industrial vision systems, applications and tools, Image and Vision Computing, vol.21, issue.2, pp.171-188, 2003.

F. Massa, B. C. Russell, A. , and M. , Deep exemplar 2d-3d detection by adapting from real to rendered views, CVPR, pp.6024-6033, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01801049

D. Maturana and S. Scherer, Voxnet: A 3d convolutional neural network for real-time object recognition, IROS, pp.922-928, 2015.

T. Nguyen, J. Nebel, and F. Florez-revuelta, Recognition of activities of daily living with egocentric vision: A review, Sensors, vol.16, issue.1, p.72, 2016.

X. Peng, B. Sun, K. Ali, and K. Saenko, Learning deep object detectors from 3d models, ICCV, pp.1278-1286, 2015.

B. Planche, S. Zakharov, Z. Wu, H. Kosch, and S. Ilic, Seeing beyond appearance -mapping real images into geometrical domains for unsupervised cadbased recognition, IROS, 2019.

F. Qin, L. Li, S. Gao, X. Yang, C. et al., A deep learning approach to the classification of 3d cad models, Journal of Zhejiang University SCIENCE C, vol.15, issue.2, pp.91-106, 2014.

A. S. Razavian, H. Azizpour, J. Sullivan, C. , and S. , Cnn features off-the-shelf: an astounding baseline for recognition, CVPR Workshops, pp.806-813, 2014.

J. Redmon, A. Bochkovskiy, and S. Sinigardi, Darknet: Yolov3 -neural network for object detection, 2019.

J. Redmon and A. Farhadi, Yolov3: An incremental improvement, 2018.

A. Rozantsev, V. Lepetit, and P. Fua, On rendering synthetic images for training an object detector. Computer Vision and Image Understanding, vol.137, pp.24-37, 2015.

K. Sarkar, K. Varanasi, D. Stricker, K. Sarkar, K. Varanasi et al., Trained 3d models for cnn based object recognition, VISAPP, pp.130-137, 2017.

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, ICCV, pp.2686-2694, 2015.

M. Sundermeyer, Z. Marton, M. Durner, M. Brucker, and R. Triebel, Implicit 3d orientation learning for 6d object detection from rgb images, ECCV, pp.699-715, 2018.

A. Toshev, A. Makadia, and K. Daniilidis, Shapebased object recognition in videos using 3d synthetic object models, CVPR, pp.288-295, 2009.

J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V. Jampani et al., Training deep networks with synthetic data: Bridging the reality gap by domain randomization, CVPR Workshops, pp.969-977, 2018.

M. Ulrich, C. Steger, A. Baumgartner, and H. Ebner, Real-time object recognition in digital images for industrial applications, 5th Conference on Optical 3-D Measurement Techniques, pp.308-318, 2001.

D. W. Van-krevelen and R. Poelman, A survey of augmented reality technologies, applications and limitations, International Journal of Virtual Reality, vol.9, issue.2
URL : https://hal.archives-ouvertes.fr/hal-01530500

P. Wang, C. Sun, Y. Liu, and X. Tong, Adaptive o-cnn: A patch-based deep representation of 3d shapes, ACM Transactions on Graphics, vol.37, issue.6, p.217, 2019.

P. Wohlhart and V. Lepetit, Learning descriptors for object recognition and 3d pose estimation, CVPR, pp.3109-3118, 2015.

Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang et al., 3d shapenets: A deep representation for volumetric shapes, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1912-1920, 2015.

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, pp.75-82, 2014.