G. Adiv, Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.7, issue.4, pp.384-401, 1985.
DOI : 10.1109/TPAMI.1985.4767678

P. Anandan, A computational framework and an algorithm for the measurement of visual motion, International Journal of Computer Vision, vol.27, issue.4, pp.283-310, 1989.
DOI : 10.1007/BF00158167

J. Aujol, Some First-Order Algorithms for Total Variation Based Image Restoration, Journal of Mathematical Imaging and Vision, vol.33, issue.2, pp.307-327, 2009.
DOI : 10.1007/s10851-009-0149-y
URL : https://hal.archives-ouvertes.fr/hal-00260494

S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-255, 2004.
DOI : 10.1023/B:VISI.0000011205.11775.fd

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black et al., A database and evaluation methodology for optical flow, International Conference on Computer Vision, 2007.

T. Basha, Y. Moses, and N. Kiryati, Multi-view scene flow estimation: A view centered variational approach, Conference on Computer Vision and Pattern Recognition, pp.1506-1513, 2010.

J. Bergen, P. Anandan, K. Hanna, and R. Hingorani, Hierarchical model-based motion estimation, European Conference on Computer Vision, 1992.
DOI : 10.1007/3-540-55426-2_27

A. Bermudez and C. Moreno, Duality methods for solving variational inequalities, Computers and Mathematics with Applications, pp.43-58, 1981.
DOI : 10.1016/0898-1221(81)90006-7

P. Besl and N. D. Mckay, A method for registration of 3-D shapes, Pattern Analysis and Machine Intelligence, pp.239-256, 1992.
DOI : 10.1109/34.121791

K. Bredies, K. Kunisch, and T. Pock, Total Generalized Variation, SIAM Journal on Imaging Sciences, vol.3, issue.3, pp.492-526, 2010.
DOI : 10.1137/090769521
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.6330

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, 2004.
DOI : 10.1007/978-3-540-24673-2_3

T. Brox and J. Malik, Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.500-513, 2011.
DOI : 10.1109/TPAMI.2010.143

A. Bruhn, J. Weickert, A. Schñ, and C. ¶rr, Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods, International Journal of Computer Vision, vol.61, issue.3, pp.61211-231, 2005.
DOI : 10.1023/B:VISI.0000045324.43199.43
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.7916

D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, A Naturalistic Open Source Movie for Optical Flow Evaluation, Proceedings of the 12th European Conference on Computer Vision -Volume Part VI, pp.611-625
DOI : 10.1007/978-3-642-33783-3_44

R. Carceroni and K. Kutulakos, Multi-view scene captured by surfer sampling: From video streams to non-rigid 3D motion, In International Journal of Computer Vision, vol.110, pp.75-90, 2008.

A. Chambolle, An algorithm for total variation minimization and applications, In Journal of Mathematical Imaging and Vision, vol.20, issue.12, pp.89-97, 2004.

A. Chambolle, Total Variation Minimization and a Class of Binary MRF Models, Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.136-152, 2005.
DOI : 10.1007/11585978_10

A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011.
DOI : 10.1007/s10851-010-0251-1
URL : https://hal.archives-ouvertes.fr/hal-00490826

T. F. Chan, G. H. Golub, and P. Mulet, A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration, SIAM Journal on Scientific Computing, vol.20, issue.6, pp.1964-1977, 1999.
DOI : 10.1137/S1064827596299767

B. Curless and M. Levoy, A volumetric method for building complex models from range images, Proceedings of the 23rd annual conference on Computer graphics and interactive techniques , SIGGRAPH '96, pp.303-312, 1996.
DOI : 10.1145/237170.237269

F. Devernay, D. Mateus, and M. Guilbert, Multi-camera scene flow by tracking 3D points and surfels, Conference on Computer Vision and Pattern Recognition, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00262285

J. Fang and T. Huang, Solving three-dimensional small-rotation motion equations, Conference on Computer Vision and Pattern Recognition, 1983.

Y. Fukurawa and J. Ponce, Dense 3D motion capture from synchronized video streams, Conference on Computer Vision and Pattern Recognition, 2008.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248074

B. Goldluecke and D. Cremers, An approach to vectorial total variation based on geometric measure theory, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.327-333, 2010.
DOI : 10.1109/CVPR.2010.5540194

B. Goldluecke, E. Strekalovskiy, and D. Cremers, The Natural Vectorial Total Variation Which Arises from Geometric Measure Theory, SIAM Journal on Imaging Sciences, vol.5, issue.2, pp.537-563, 2012.
DOI : 10.1137/110823766

P. J. Green, Iteratively Reweighted Least Squares for Maximum Likelihood Estimation, and some Robust and Resistant Alternatives, In Journal of the Royal Statistical Society. Series B (Methodological), vol.46, issue.2, 1984.

S. Hadfield and R. Bowden, Kinecting the dots: Particle based scene flow from depth sensors, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126509

S. Hadfield and R. Bowden, Scene Particles: Unregularized Particle-Based Scene Flow Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.3, 2014.
DOI : 10.1109/TPAMI.2013.162

E. Herbst, X. Ren, and D. Fox, RGB-D flow: Dense 3-D motion estimation using color and depth, 2013 IEEE International Conference on Robotics and Automation, pp.2276-2282, 2013.
DOI : 10.1109/ICRA.2013.6630885

D. Herrera, J. Kannala, and J. Heikkila, Accurate and Practical Calibration of a Depth and Color Camera Pair, International Conference on Computer Analysis of Images and Patterns, 2011.
DOI : 10.1017/CBO9780511811685

B. K. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

B. Horn and E. Weldon, Direct methods for recovering motion, International Journal of Computer Vision, vol.208, issue.3, pp.51-76, 1988.
DOI : 10.1007/BF00836281

M. Hornacek, A. Fitzgibbon, and C. Rother, SphereFlow: 6 DoF Scene Flow from RGB-D Pairs, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.451

M. Hornacek, C. Rhemann, M. Gelautz, and C. Rother, Depth Super Resolution by Rigid Body Self-Similarity in 3D, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.1123-1130, 2013.
DOI : 10.1109/CVPR.2013.149

F. Huguet and F. Devernay, A Variational Method for Scene Flow Estimation from Stereo Sequences, 2007 IEEE 11th International Conference on Computer Vision, pp.1-7, 2007.
DOI : 10.1109/ICCV.2007.4409000
URL : https://hal.archives-ouvertes.fr/inria-00166589

C. Kerl, J. Sturm, and D. Cremers, Robust odometry estimation for RGB-D cameras, 2013 IEEE International Conference on Robotics and Automation, pp.3748-3754, 2013.
DOI : 10.1109/ICRA.2013.6631104

K. Khoshelham and S. Elberink, Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications, Sensors, pp.1437-1454, 2012.
DOI : 10.3390/s120201437

I. Laptev and T. Lindeberg, Space-time interest points, European Conference on Computer Vision, 2003.

J. Lellmann, E. Strekalovskiy, S. Koetter, and D. Cremers, Total Variation Regularization for Functions with Values in a Manifold, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.366

V. Lempitsky, S. Roth, and C. Rother, FusionFlow: Discrete-continuous optimization for optical flow estimation, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587751

A. Letouzey, B. Petit, and E. Boyer, Scene Flow from Depth and Color Images, Procedings of the British Machine Vision Conference 2011, 2011.
DOI : 10.5244/C.25.46
URL : https://hal.archives-ouvertes.fr/inria-00616353

H. Li, B. Adams, L. J. Guibas, and M. Pauly, Robust single-view geometry and motion reconstruction, ACM SIGGRAPH Asia, vol.175, pp.1-17510, 2009.

H. Longuet-higgins and K. Prazdny, The Interpretation of a Moving Retinal Image, Proceedings of the Royal Society B: Biological Sciences, vol.208, issue.1173, pp.385-397, 1980.
DOI : 10.1098/rspb.1980.0057

B. D. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proceedings of the 7th International Joint Conference on Artificial Intelligence - IJCAI'81, pp.674-679, 1981.

T. Lukins and R. Fisher, Colour Constrained 4D Flow, Procedings of the British Machine Vision Conference 2005, 2005.
DOI : 10.5244/C.19.31

P. Matikainen, M. Hebert, and R. Sukthankar, Trajectons: Action recognition through the motion analysis of tracked features, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009.
DOI : 10.1109/ICCVW.2009.5457659

R. Messing, C. Pal, and H. Kautz, Activity recognition using the velocity histories of tracked keypoints, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459154

J. Neumann and Y. Aloimonos, Spatio-temporal stereo using multi-resolution division surfaces, In International Journal of Computer Vision, pp.181-193, 2002.

R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim et al., KinectFusion: Real-time dense surface mapping and tracking, 2011 10th IEEE International Symposium on Mixed and Augmented Reality, 2011.
DOI : 10.1109/ISMAR.2011.6092378
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.100

J. Niebles, C. Chen, and L. Fei-fei, Modelling temporal structure of decomposable motion segments for activity classification, European Conference on Computer Vision, 2010.

T. Nir, A. M. Bruckstein, and R. Kimmel, Over-Parameterized Variational Optical Flow, International Journal of Computer Vision, vol.14, issue.3, pp.205-216, 2008.
DOI : 10.1007/s11263-007-0051-2

J. Quiroga, T. Brox, F. Devernay, and J. Crowley, Dense Semi-rigid Scene Flow Estimation from RGBD Images, European Conference on Computer Vision, 2014.
DOI : 10.1007/978-3-319-10584-0_37
URL : https://hal.archives-ouvertes.fr/hal-01021925

J. Quiroga, F. Devernay, and J. Crowley, Scene flow by tracking in intensity and depth data Computer Vision and Pattern Recognition Workshops (CVPRW), pp.50-57, 2012.

J. Quiroga, F. Devernay, and J. Crowley, Local/global scene flow estimation, 2013 IEEE International Conference on Image Processing, pp.3850-3854, 2013.
DOI : 10.1109/ICIP.2013.6738793
URL : https://hal.archives-ouvertes.fr/hal-00829515

J. Quiroga, F. Devernay, and J. Crowley, Local scene flow by tracking in intensity and depth, Journal of Visual Communication and Image Representation, vol.25, issue.1, pp.98-107, 2014.
DOI : 10.1016/j.jvcir.2013.03.018
URL : https://hal.archives-ouvertes.fr/hal-00817011

C. Richardt, C. Stoll, N. A. Dodgson, H. P. Seidel, and C. Theobalt, Coherent Spatiotemporal Filtering, Upsampling and Rendering of RGBZ Videos, Computer Graphics Forum, vol.21, issue.8, pp.31247-256, 2012.
DOI : 10.1111/j.1467-8659.2012.03003.x

G. Rosman, A. Bronstein, M. Bronstein, X. C. Tai, and R. Kimmel, Group-Valued Regularization for Analysis of Articulated Motion, European Conference on Computer Vision Workshops, pp.52-62, 2012.
DOI : 10.1007/978-3-642-33863-2_6

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Proceedings of the Eleventh Annual International Conference of the Center for Nonlinear Studies on Experimental Mathematics, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

D. Scharstein and R. Szeliski, High-accuracy stereo depth maps using structured light, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.195-202, 2003.
DOI : 10.1109/CVPR.2003.1211354
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.6857

J. Shi and C. Tomasi, Good features to track, Conference on Computer Vision and Pattern Recognition, 1994.

J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio et al., Real-time human pose recognition in parts from a single depth image, Conference on Computer Vision and Pattern Recongintion, 2011.

H. Spies, B. Jahne, and J. Barron, Dense range flow from depth and intensity data, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.131-134, 2000.
DOI : 10.1109/ICPR.2000.905290
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.3327

J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, A benchmark for the evaluation of RGB-D SLAM systems, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.573-580, 2012.
DOI : 10.1109/IROS.2012.6385773

D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2432-2439, 2010.
DOI : 10.1109/CVPR.2010.5539939

L. Valgaerts, A. Bruhn, H. Zimmer, J. Weickert, C. Stoll et al., Joint Estimation of Motion, Structure and Geometry from Stereo Sequences, European Conference on Computer Vision, 2010.
DOI : 10.1007/978-3-642-15561-1_41

S. Vedula, S. Baker, P. Rander, and R. Collins, Three-dimensional scene flow, International Conference on Computer Vision, pp.722-729, 1999.
DOI : 10.1109/iccv.1999.790293
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.3563

S. Vedula, S. Baker, P. Render, R. Collins, and T. Kanade, Threedimensional scene flow, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, pp.275-280, 2005.

C. Vogel, K. Schindler, and S. Roth, 3D scene flow estimation with a rigid motion prior, 2011 International Conference on Computer Vision, pp.1291-1298, 2011.
DOI : 10.1109/ICCV.2011.6126381

C. Vogel, S. Roth, and K. Schindler, View-Consistent 3D Scene Flow Estimation over Multiple Frames, European Conference on Computer Vision, pp.263-278, 2014.
DOI : 10.1007/978-3-319-10593-2_18
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.673.1987

C. Vogel, K. Schindler, and S. Roth, Piecewise Rigid Scene Flow, 2013 IEEE International Conference on Computer Vision, pp.1377-1384, 2013.
DOI : 10.1109/ICCV.2013.174

S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer, Modeling temporal coherence for optical flow, 2011 International Conference on Computer Vision, pp.1116-1123, 2011.
DOI : 10.1109/ICCV.2011.6126359

H. Wang, A. Kläser, C. Schmid, and C. L. Liu, Action recognition by dense trajectories, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995407
URL : https://hal.archives-ouvertes.fr/inria-00583818

Y. Wang, J. Yang, W. Yin, and Y. Zhang, A New Alternating Minimization Algorithm for Total Variation Image Reconstruction, SIAM Journal on Imaging Sciences, vol.1, issue.3, pp.248-272, 2008.
DOI : 10.1137/080724265

A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke et al., Efficient Dense Scene Flow from Sparse or Dense Stereo Data, European Conference on Computer Vision, pp.739-751, 2008.
DOI : 10.1007/978-3-540-88682-2_56

A. Wedel, T. Pock, C. Zach, H. Bischof, and D. Cremers, An Improved Algorithm for TV-L 1 Optical Flow, Lecture Notes in Computer Science, vol.5604, pp.23-45, 2009.
DOI : 10.1007/978-3-642-03061-1_2

M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers et al., Anisotropic Huber-L1 Optical Flow, Procedings of the British Machine Vision Conference 2009, 2009.
DOI : 10.5244/C.23.108

L. Xu, J. Jia, and Y. Matsushita, Motion detail preserving optical flow estimation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1744-1757, 2012.
DOI : 10.1109/CVPR.2010.5539820

Y. Zhang and C. Kambhamettu, On 3D scene flow and structure estimation, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.991044

M. Zhu and T. Chan, An efficient primal-dual hybrid gradient algorithm for total variation image restoration, UCLA CAM Report, pp.8-34, 2008.

M. Zhu, S. J. Wright, and T. Chan, Duality-based algorithms for totalvariation-regularized image restoration, Computational Optimization and Applications, pp.377-400, 2010.