P. Meer, D. Mintz, A. Rosenfeld, and D. Y. Kim, Robust regression methods for computer vision: A review, International journal of computer vision, vol.6, issue.1, pp.59-70, 1991.

M. A. Fischler and R. C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.

G. Puy and P. Vandergheynst, Robust image reconstruction from multiview measurements, SIAM Journal on Imaging Sciences, vol.7, issue.1, pp.128-156, 2014.

V. Belagiannis, C. Rupprecht, G. Carneiro, and N. Navab, Robust optimization for deep regression, Proceedings of the IEEE International Conference on Computer Vision, pp.2830-2838, 2015.

A. Tewari, M. Zollhöfer, H. Kim, P. Garrido, F. Bernard et al., Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction, International Conference on Computer Vision, 2017.

A. Newell, K. Yang, and J. Deng, Stacked hourglass networks for human pose estimation, European Conference on Computer Vision, pp.483-499, 2016.

D. Ciregan, U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, Computer Vision and Pattern Recognition (CVPR), 2012.

, IEEE Conference on, pp.3642-3649, 2012.

A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas et al., Flownet: Learning optical flow with convolutional networks, International Conference on Computer Vision, pp.2758-2766, 2015.
DOI : 10.1109/iccv.2015.316

URL : http://arxiv.org/pdf/1504.06852

P. H. Torr and D. W. Murray, The development and comparison of robust methods for estimating the fundamental matrix, International Journal of Computer Vision, vol.24, issue.3, pp.271-300, 1997.

P. H. Torr and A. Zisserman, Mlesac: A new robust estimator with application to estimating image geometry, Computer Vision and Image Understanding, vol.78, issue.1, pp.138-156, 2000.
DOI : 10.1006/cviu.1999.0832

URL : http://www.cs.cmu.edu/~misc-read/talks-2003/torr_mlesac.pdf

C. V. Stewart, Minpran: A new robust estimator for computer vision, Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.10, pp.925-938, 1995.
DOI : 10.1109/34.464558

P. J. Huber, Robust statistics, International Encyclopedia of Statistical Science, pp.1248-1251, 2011.
DOI : 10.1002/0471725250

URL : https://onlinelibrary.wiley.com/doi/pdf/10.1002/0471725250.fmatter

P. W. Holland and R. E. Welsch, Robust regression using iteratively reweighted leastsquares, Communications in Statistics-theory and Methods, vol.6, issue.9, pp.813-827, 1977.
DOI : 10.1080/03610927708827533

J. Odobez and P. Bouthemy, Separation of moving regions from background in an image sequence acquired with a mobile camera, Video Data Compression for Multimedia Computing, pp.283-311, 1997.

D. Cremers and S. Soatto, Motion competition: A variational approach to piecewise parametric motion segmentation, International Journal of Computer Vision, vol.62, issue.3, pp.249-265, 2005.
DOI : 10.1007/s11263-005-4882-4

URL : http://www.vision.cs.ucla.edu/papers/cremersS05.pdf

M. J. Black and A. D. Jepson, Estimating optical flow in segmented images using variable-order parametric models with local deformations, Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.10, pp.972-986, 1996.
DOI : 10.1109/34.541407

G. Farnebäck, Two-frame motion estimation based on polynomial expansion, Image Analysis, pp.363-370, 2003.

D. Fortun, P. Bouthemy, and C. Kervrann, Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow, Computer Vision and Image Understanding, vol.145, pp.1-182, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01001758

J. Yang and H. Li, Dense, accurate optical flow estimation with piecewise parametric model, Computer Vision Pattern Recognition, 2015.
DOI : 10.1109/cvpr.2015.7298704

J. M. Pérez-rúa, A. Basset, and P. Bouthemy, Detection and localization of anomalous motion in video sequences from local histograms of labeled affine flows, Frontiers in ICT, vol.4, p.10, 2017.

M. J. Black and Y. Yacoob, Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion, International Conference on Computer Vision, pp.374-381, 1995.
DOI : 10.1109/iccv.1995.466915

URL : http://www.parc.xerox.com/spl/members/black/Papers/iccv95-MajestiK.ps.Z

M. J. Black and P. Anandan, The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields, Computer Vision and Image Understanding, vol.63, issue.1, pp.75-104, 1996.
DOI : 10.1006/cviu.1996.0006

J. Bergen, P. Anandan, K. Hanna, and R. Hingorani, Hierarchical model-based motion estimation, European Conference on Computer Vision, pp.237-252, 1992.

J. M. Odobez and P. Bouthemy, Robust multiresolution estimation of parametric motion models, Journal of Visual Communication and Image Representation, vol.6, issue.4, pp.348-365, 1995.
DOI : 10.1006/jvci.1995.1029

URL : http://www.idiap.ch/~odobez/publications/OdobezBouthemy-IJVCIR1995.pdf

T. Senst, V. Eiselein, and T. Sikora, Robust local optical flow for feature tracking, Transactions on Circuits and Systems for Video Technology, vol.22, issue.9, pp.1377-1387, 2012.
DOI : 10.1109/tcsvt.2012.2202070

J. Thewlis, S. Zheng, P. H. Torr, and A. Vedaldi, Fully-trainable deep matching. British Machine Vision Conference, 2016.
DOI : 10.5244/c.30.145

URL : http://www.bmva.org/bmvc/2016/papers/paper145/abstract145.pdf

E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy et al., Flownet 2.0: Evolution of optical flow estimation with deep networks, Computer Vision Pattern Recognition, 2017.

C. Bailer, K. Varanasi, and D. Stricker, Cnn-based patch matching for optical flow with thresholded hinge embedding loss, Computer Vision Pattern Recognition, 2017.
DOI : 10.1109/cvpr.2017.290

URL : http://arxiv.org/pdf/1607.08064

D. Sun, X. Yang, M. Y. Liu, and J. Kautz, Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume, 2017.

G. Farnebäck, Fast and accurate motion estimation using orientation tensors and parametric motion models, International Conference on Pattern Recognition, vol.1, pp.135-139, 2000.

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, Deepflow: Large displacement optical flow with deep matching, International Conference on Computer Vision, pp.1385-1392, 2013.
DOI : 10.1109/iccv.2013.175

URL : https://hal.archives-ouvertes.fr/hal-00873592

I. Rocco, R. Arandjelovi´carandjelovi´c, and J. Sivic, Convolutional neural network architecture for geometric matching, Computer Vision Pattern Recognition, 2017.
DOI : 10.1109/cvpr.2017.12

URL : https://hal.archives-ouvertes.fr/hal-01859616

F. L. Bookstein, Principal warps: Thin-plate splines and the decomposition of deformations, Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.6, pp.567-585, 1989.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Computer Vision Pattern Recognition, pp.770-778, 2016.
DOI : 10.1109/cvpr.2016.90

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

K. He, X. Zhang, S. Ren, and J. Sun, Identity mappings in deep residual networks, European Conference on Computer Vision, pp.630-645, 2016.
DOI : 10.1007/978-3-319-46493-0_38

URL : http://arxiv.org/pdf/1603.05027

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, The Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.

V. Jain and S. Seung, Natural image denoising with convolutional networks, Conference on Neural Information Processing Systems, pp.769-776, 2009.

S. Liu, L. Yuan, P. Tan, and J. Sun, Steadyflow: Spatially smooth optical flow for video stabilization, Computer Vision Pattern Recognition, pp.4209-4216, 2014.
DOI : 10.1109/cvpr.2014.536

URL : http://research.microsoft.com/en-us/um/people/jiansun/papers/CVPR14_SteadyFlow.pdf

D. Detone, T. Malisiewicz, and A. Rabinovich, Superpoint: Self-supervised interest point detection and description, 2017.

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, High accuracy optical flow estimation based on a theory for warping, European Conference on Computer Vision, pp.25-36, 2004.
DOI : 10.1007/978-3-540-24673-2_3

URL : http://www.mia.uni-saarland.de/brox/OpticFlowWarping.pdf