N. Hirose, F. Xia, R. Martín-martín, A. Sadeghian, and S. Savarese, Deep Visual MPC-Policy Learning for Navigation, IEEE Robotics and Automation Letters, vol.4, issue.4, pp.3184-3191, 2019.

K. Saitoh, T. Machida, K. Kiyokawa, and H. Takemura, A 2d-3d integrated interface for mobile robot control using omnidirectional images and 3d geometric models, IEEE/ACM International Symposium on Mixed and Augmented Reality, pp.173-176, 2006.

D. Caruso, J. Engel, and D. Cremers, Large-scale direct SLAM for omnidirectional cameras, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.141-148, 2015.

F. Pearson, Map projections : theory and applications, 1990.

E. Gaba, Tissot indicatrix equirectangular projection, 2008.

B. K. Horn and B. G. Schunck, Determining Optical Flow, Artificial Intelligence, vol.17, pp.185-203, 1981.

A. Radgui, C. Demonceaux, E. Mouaddib, M. Rziza, and D. Aboutajdine, Optical flow estimation from multichannel spherical image decomposition, Computer Vision and Image Understanding, vol.115, pp.1263-1272, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00637423

A. Dosovitskiy, P. Fischer, E. Ilg, P. Haüsser, C. Haz?rbas et al., FlowNet: Learning Optical Flow with Convolutional Networks, 2015.

E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy et al., FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1647-1655, 2017.

T. Hui, X. Tang, and C. C. Loy, LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.8981-8989, 2018.

, A Lightweight Optical Flow CNN -Revisiting Data Fidelity and Regularization, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1-1

D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, A naturalistic open source movie for optical flow evaluation, European Conf. on Computer Vision (ECCV), pp.611-625, 2012.

M. Menze and A. Geiger, Object Scene Flow for Autonomous Vehicles, Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

T. S. Cohen, M. Geiger, J. Köhler, and M. Welling, Spherical CNNs, 2018.

Y. K. Lee, J. Jeong, J. S. Yun, C. W. June, and K. Jin-yoon, SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images, CVPR, vol.11, 2018.

Y. Su and K. Grauman, Learning Spherical Convolution for Fast Features from 360 Imagery, Advances in Neural Information Processing Systems, vol.30, pp.529-539, 2017.

K. Tateno, N. Navab, and F. Tombari, Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images, The European Conference on Computer Vision (ECCV), pp.732-750, 2018.

B. Coors, A. P. Condurache, and A. Geiger, SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images, The European Conference on Computer Vision (ECCV), pp.525-541, 2018.

C. Fernandez, J. Facil, A. Perez-yus, C. Demonceaux, J. Civera et al., Corners for Layout: End-to-End Layout Recovery from 360 Images, IEEE Robotics and Automation Letters, vol.01, issue.2020, pp.1255-1262
URL : https://hal.archives-ouvertes.fr/hal-02140693

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe: Convolutional Architecture for Fast Feature Embedding, MM 2014 -Proceedings of the 2014 ACM Conference on Multimedia, vol.06, 2014.

N. Mayer, E. Ilg, P. Haüsser, P. Fischer, D. Cremers et al., A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4040-4048, 2016.

A. Ranjan, D. Hoffmann, D. Tzionas, S. Tang, J. Romero et al., Learning Multi-Human Optical Flow, International Journal of Computer Vision, vol.01, issue.2020

S. Baker, S. Roth, D. Scharstein, M. J. Black, J. P. Lewis et al., A Database and Evaluation Methodology for Optical Flow, International Conference on Computer Vision, vol.92, 2007.