Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes, RSS, 2018.

M. Rad, M. Oberweger, and V. Lepetit, Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images, In: CVPR, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02506574

B. Tekin, S. N. Sinha, and P. Fua, Real-Time Seamless Single Shot 6D Object Pose Prediction, In: CVPR, 2018.

M. Rad and V. Lepetit, BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects Without Using Depth, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02506354

M. Oberweger and V. Lepetit, DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation, ICCV Workshops, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02506350

S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski et al., Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes, 2012.

T. Hodan, P. Haluza, S. Obdrzalek, J. Matas, M. Lourakis et al., T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects, 2017.

B. Calli, A. Singh, J. Bruce, A. Walsman, K. Konolige et al., Yale-CMU-Berkeley Dataset for Robotic Manipulation Research, IJRR, vol.36, pp.261-268, 2017.

K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan, Domain Separation Networks. In: NIPS, 2016.

I. J. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative Adversarial Networks. In: NIPS, 2014.

A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang et al., Learning from Simulated and Unsupervised Images through Adversarial Training, 2016.

J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, 2017.

Y. Ganin and V. Lempitsky, Unsupervised Domain Adaption by Backpropagation, ICML, 2015.

K. Muandet, D. Balduzzi, and B. Schölkopf, Domain Generalization via Invariant Feature Representation, ICML, 2013.

S. Pan, I. Tsang, J. Kwok, and Q. Yang, Domain Adaptation via Transfer Component Analysis, 2009.

A. Rozantsev, M. Salzmann, and P. Fua, Beyond Sharing Weights for Deep Domain Adaptation, 2017.

A. Gretton, K. Borgwardt, M. J. Rasch, B. Schölkopf, and A. J. Smola, A Kernel Method for the Two-Sample Problem, 2006.

J. Zhang, J. Jiao, M. Chen, L. Qu, X. Xu et al., 3D Hand Pose Tracking and Estimation Using Stereo Matching. ARXIV, 2016.

F. Müller, D. Mehta, O. Sotnychenko, S. Sridhar, D. Casas et al., Realtime Hand Tracking under Occlusion from an Egocentric RGB-D Sensor, 2017.

A. Krull, E. Brachmann, F. Michel, M. Y. Yang, S. Gumhold et al., Learning Analysis-By-Synthesis for 6D Pose Estimation in RGB-D Images, 2015.

M. Oberweger, M. Rad, and V. Lepetit, Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation, In: ECCV, 2018.

C. Zimmermann and T. Brox, Learning to Estimate 3D Hand Pose from Single RGB Images, 2017.

F. Müller, F. Bernard, O. Sotnychenko, D. Mehta, S. Sridhar et al., GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB, In: CVPR, 2018.

P. Panteleris, I. Oikonomidis, and A. Argyros, Using a single RGB frame for real time 3D hand pose estimation in the wild, In: WACV, 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, 2017.

M. Sundermeyer, Z. C. Marton, M. Durner, M. Brucker, and R. Triebel, Implicit 3d orientation learning for 6d object detection from rgb images, Proceedings of the European Conference on Computer Vision (ECCV), 2018.

S. Hinterstoisser, V. Lepetit, P. Wohlhart, and K. Konolige, On Pre-Trained Image Features and Synthetic Images for Deep Learning, 2017.

D. P. Kingma and M. Welling, Auto-Encoding Variational Bayes, 2014.

A. Spurr, J. Song, S. Park, and O. Hilliges, Cross-modal Deep Variational Hand Pose Estimation, In: CVPR, 2018.

S. Gupta, J. Hoffman, and J. Malik, Cross Modal Distillation for Supervision Transfer, CVPR, 2016.

G. Cai, Y. Wang, M. Zhou, and L. He, Unsupervised Domain Adaptation with, Adversarial Residual Transform Networks. ARXIV, 2018.

S. Zakharov, B. Planche, Z. Wu, A. Hutter, H. Kosch et al., Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only. ARXIV, 2018.

J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba et al., Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World, 2017.

J. Hoffman, S. Gupta, J. Leong, S. Guadarrama, and T. Darrell, Cross-Modal Adaptation for RGB-D Detection, 2016.

X. Song, S. Jiang, and L. Herranz, Combining Models from Multiple Sources for RGB-D Scene Recognition, 2017.

X. Huang, Y. Peng, and M. Yuan, Cross-modal Common Representation Learning by Hybrid Transfer Network, 2017.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016.

K. Simonyan, A. Vedaldi, and A. Zisserman, Learning Local Feature Descriptors Using Convex Optimisation, 2014.

K. He, X. Zhang, R. Ren, and J. Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification, 2015.

D. Eigen, C. Puhrsch, and R. Fergus, Depth Map Prediction from a Single Image Using a Multi-Scale Deep Network, NIPS, 2014.

D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, ICML, 2015.

E. Brachmann, F. Michel, A. Krull, M. M. Yang, S. Gumhold et al., Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image, 2016.

K. Lai, L. Bo, X. Ren, and D. Fox, A large-scale hierarchical multi-view rgb-d object dataset, 2011.

J. Tompson, M. Stein, Y. Lecun, and K. Perlin, Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks, TOG, vol.33, 2014.

R. Zhao, Y. Wang, and A. Martinez, A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image, 2016.