L. V. Ahlfors, Lectures on Quasiconformal Mappings, 2006.
DOI : 10.1090/ulect/038

R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications, 1993.

A. A. Amini, T. E. Weymouth, and R. C. Jain, Using dynamic programming for solving variational problems in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.9, pp.855-867, 1990.
DOI : 10.1109/34.57681

D. Anguelov, P. Srinivasan, H. Pang, D. Koller, S. Thrun et al., The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces, Proc. Neural Information Processing Systems, 2004.

S. Belongie, J. Malik, and J. Puzicha, Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.4, pp.509-522, 2002.
DOI : 10.1109/34.993558

A. C. Berg, T. L. Berg, and J. Malik, Shape Matching and Object Recognition Using Low Distortion Correspondences, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.26-33, 2005.
DOI : 10.1109/CVPR.2005.320

P. J. Besl and N. D. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1992.
DOI : 10.1109/34.121791

A. Blake, P. Kohli, and C. Rother, Markov Random Fields for Vision and Image Processing, 2011.

E. Boros and P. L. Hammer, Pseudo-Boolean optimization, Discrete Applied Mathematics, vol.123, issue.1-3, pp.155-225, 2002.
DOI : 10.1016/S0166-218X(01)00341-9
URL : https://hal.archives-ouvertes.fr/hal-01150533

S. P. Boyd and L. Vandenberghe, Convex Optimization, 2004.

S. C. Brenner and R. Scott, The Mathematical Theory of Finite Element Methods, 2007.

A. M. Bronstein, M. M. Bronstein, and R. Kimmel, Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching, Proc. National Academy of Sciences, pp.1168-1172, 2006.
DOI : 10.1073/pnas.0508601103

A. M. Bronstein, M. M. Bronstein, and R. Kimmel, Expression-Invariant Representations of Faces, IEEE Transactions on Image Processing, vol.16, issue.1, pp.1042-1053, 2004.
DOI : 10.1109/TIP.2006.884940

A. M. Bronstein, M. M. Bronstein, and R. Kimmel, Numerical Geometry of Non-Rigid Shapes, 2008.
DOI : 10.1007/978-0-387-73301-2

B. J. Brown and S. Rusinkiewicz, Global non-rigid alignment of 3-D scans, ACM Trans. Graph, vol.26, 2007.

R. J. Campbell and P. J. Flynn, A Survey Of Free-Form Object Representation and Recognition Techniques, Computer Vision and Image Understanding, vol.81, issue.2, pp.166-210, 2001.
DOI : 10.1006/cviu.2000.0889

M. Cho, J. Lee, and K. M. Lee, Feature correspondence and deformable object matching via agglomerative correspondence clustering, Proc. IEEE Int'l Conf. Computer Vision, 2009.

M. Cho and K. M. Lee, Progressive graph matching: Making a move of graphs via probabilistic voting, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

T. Cour, P. Srinivasan, and J. Shi, Balanced graph matching, Proc. Neural Information Processing Systems, 2007.

O. Duchenne, F. Bach, I. Kweon, and J. Ponce, A tensor-based algorithm for high-order graph matching, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01063322

H. M. Farkas and I. Kra, Riemann Surfaces, 2004.

A. Fix, J. Chen, E. Boros, and R. Zabih, Approximate MRF Inference Using Bounded Treewidth Subgraphs, Proc. European Conf. Computer Vision, pp.385-398, 2012.
DOI : 10.1007/978-3-642-33718-5_28

B. Glocker, T. H. Heibel, N. Navab, P. Kohli, and C. Rother, TriangleFlow: Optical Flow with Triangulation-Based Higher-Order Likelihoods, Proc. European Conf. on Computer Vision, 2010.
DOI : 10.1007/978-3-642-15558-1_20

Q. Huang, B. Adams, M. Wicke, and L. J. Guibas, Non-Rigid Registration Under Isometric Deformations, Symposium on Geometry Processing, pp.1449-1457, 2008.
DOI : 10.1111/j.1467-8659.2008.01285.x
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.674.6909

X. Huang, N. Paragios, and D. N. Metaxas, Establishing Local Correspondences towards Compact Representations of Anatomical Structures, International Conference, 2003.
DOI : 10.1007/978-3-540-39903-2_113

H. Ishikawa, Higher-order clique reduction in binary graph cut, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206689

A. Keane, CUDA (compute unified device architecture), 2006.

V. G. Kim, Y. Lipman, and T. Funkhouser, Blended intrinsic maps, Proc. of SIGGRAPH 2011), pp.79-80, 2011.

V. Kolmogorov and C. Rother, Minimizing Nonsubmodular Functions with Graph Cuts-A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1274-1279, 2007.
DOI : 10.1109/TPAMI.2007.1031

N. Komodakis and N. Paragios, Beyond pairwise energies: Efficient optimization for higher-order MRFs, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206846

N. Komodakis, N. Paragios, and G. Tziritas, MRF Energy Minimization and Beyond via Dual Decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.531-552, 2011.
DOI : 10.1109/TPAMI.2010.108
URL : https://hal.archives-ouvertes.fr/hal-00856311

N. Komodakis, G. Tziritas, and N. Paragios, Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies, Computer Vision and Image Understanding, vol.112, issue.1, pp.14-29, 2008.
DOI : 10.1016/j.cviu.2008.06.007
URL : https://hal.archives-ouvertes.fr/hal-00918699

N. Komodakis, B. Xiang, and N. Paragios, A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.7, pp.1425-1441, 2015.
DOI : 10.1109/TPAMI.2014.2368990
URL : https://hal.archives-ouvertes.fr/hal-01245673

J. Lee, M. Cho, and K. M. Lee, Hyper-graph matching via reweighted random walks, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995387
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.4627

M. Leordeanu and M. Hebert, A spectral technique for correspondence problems using pairwise constraints, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1482-1489, 2005.
DOI : 10.1109/ICCV.2005.20

M. Leordeanu, A. Zanfir, and C. Sminchisescu, Semi-supervised learning and optimization for hypergraph matching, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126507

Y. Lipman and T. Funkhouser, Möbius voting for surface correspondence, ACM Trans. Graph, vol.2872, issue.3, pp.1-7212, 2009.

M. Ovsjanikov, M. Ben-chen, J. Solomon, A. Butscher, and L. Guibas, Functional maps, ACM Transactions on Graphics, vol.31, issue.4, pp.1-3011, 2012.
DOI : 10.1145/2185520.2185526

M. Ovsjanikov, Q. Merigot, F. Memoli, and L. J. Guibas, One Point Isometric Matching with the Heat Kernel, Computer Graphics Forum, vol.27, issue.5, pp.1555-1564, 2010.
DOI : 10.1111/j.1467-8659.2010.01764.x
URL : https://hal.archives-ouvertes.fr/hal-00543885

N. Paragios, M. Rousson, and V. Ramesh, Non-rigid registration using distance functions, Computer Vision and Image Understanding, vol.89, issue.2-3, pp.142-165, 2003.
DOI : 10.1016/S1077-3142(03)00010-9

U. Pinkall and K. Polthier, Computing Discrete Minimal Surfaces and Their Conjugates, Experimental Mathematics, vol.3, issue.1, pp.15-36, 1993.
DOI : 10.2307/1970625
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.6708

P. V. Sander, J. Snyder, S. J. Gortler, and H. Hoppe, Texture mapping progressive meshes, Proceedings of the 28th annual conference on Computer graphics and interactive techniques , SIGGRAPH '01, pp.409-416, 2001.
DOI : 10.1145/383259.383307
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.8663

C. Schellewald and C. Schnorr, Probabilistic Subgraph Matching Based on Convex Relaxation, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2005.
DOI : 10.1007/11585978_12

A. Shaji, A. Varol, L. Torresani, and P. Fua, Simultaneous point matching and 3D deformable surface reconstruction, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539827
URL : http://infoscience.epfl.ch/record/148055

A. Sharma, R. P. Horaud, J. Cech, and E. Boyer, Topologically-robust 3D shape matching based on diffusion geometry and seed growing, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995455
URL : https://hal.archives-ouvertes.fr/inria-00590280

A. Sheffer, E. Praun, and K. Rose, Mesh Parameterization Methods and Their Applications, Foundations and Trends?? in Computer Graphics and Vision, vol.2, issue.2, pp.105-171, 2006.
DOI : 10.1561/0600000011
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.1016

O. Sorkine and M. Alexa, As-rigid-as-possible surface modeling, Symposium on Geometry Processing, pp.109-116, 2007.

R. W. Sumner and J. Popovi´cpopovi´c, Deformation transfer for triangle meshes, SIGGRAPH, pp.399-405, 2004.
DOI : 10.1145/1186562.1015736

A. Tevs, A. Berner, M. Wand, I. Ihrke, and H. Seidel, Intrinsic Shape Matching by Planned Landmark Sampling, Eurographics, pp.543-552, 2011.
DOI : 10.1111/j.1467-8659.2011.01879.x

A. Tevs, M. Bokeloh, M. Wand, A. Schilling, and H. Seidel, Isometric registration of ambiguous and partial data, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1185-1192, 2009.
DOI : 10.1109/CVPR.2009.5206775

N. Thorstensen and R. Keriven, Non-rigid Shape Matching Using Geometry and Photometry, Proc. Asian Conf. Computer Vision, 2009.
DOI : 10.1007/978-3-642-12297-2_62
URL : https://hal.archives-ouvertes.fr/hal-00711070

P. H. Torr, Solving Markov Random Fields using semi definite programming, Ninth Int'l Workshop on Artificial Intelligence and Statistics, 2003.

L. Torresani, V. Kolmogorov, and C. Rother, Feature Correspondence Via Graph Matching: Models and Global Optimization, Proc. European Conf. on Computer Vision, pp.596-609, 2008.
DOI : 10.1007/978-3-540-88688-4_44
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.146.4872

D. Vlasic, M. Brand, H. Pfister, and J. Popovi´cpopovi´c, Face transfer with multilinear models, ACM Transactions on Graphics, vol.24, issue.3, pp.426-433, 2005.
DOI : 10.1145/1073204.1073209
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.9158

C. Wang, M. M. Bronstein, A. M. Bronstein, and N. Paragios, Discrete Minimum Distortion Correspondence Problems for Non-rigid Shape Matching, International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2011.
DOI : 10.1007/978-3-642-24785-9_49
URL : https://hal.archives-ouvertes.fr/inria-00498591

C. Wang, N. Komodakis, and N. Paragios, Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey, Computer Vision and Image Understanding, vol.117, issue.11, pp.1610-1627, 2013.
DOI : 10.1016/j.cviu.2013.07.004
URL : https://hal.archives-ouvertes.fr/hal-00858390

C. Wang, O. Teboul, F. Michel, S. Essafi, and N. Paragios, 3D Knowledge-Based Segmentation Using Pose-Invariant Higher-Order Graphs, International Conference, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2010.
DOI : 10.1007/978-3-642-15711-0_24
URL : https://hal.archives-ouvertes.fr/hal-00856051

C. Wang, Y. Zeng, L. Simon, I. Kakadiaris, D. Samaras et al., Viewpoint invariant 3D landmark model inference from monocular 2D images using higher-order priors, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126258
URL : https://hal.archives-ouvertes.fr/hal-00856131

S. Wang, Y. Wang, M. Jin, X. D. Gu, and D. Samaras, Conformal Geometry and Its Applications on 3D Shape Matching, Recognition, and Stitching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1209-1220, 2007.
DOI : 10.1109/TPAMI.2007.1050

Y. Wang, M. Gupta, S. Zhang, S. Wang, X. Gu et al., High Resolution Tracking of Non-Rigid Motion of Densely Sampled 3D Data Using Harmonic Maps, Proc. IEEE Int'l Conf. Computer Vision, 2005.
DOI : 10.1007/s11263-007-0063-y

T. Werner, Revisiting the Linear Programming Relaxation Approach to Gibbs Energy Minimization and Weighted Constraint Satisfaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.8, pp.1474-1488, 2010.
DOI : 10.1109/TPAMI.2009.134

L. Yin, X. Chen, Y. Sun, T. Worm, and M. Reale, A high-resolution 3D dynamic facial expression database, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, 2008.
DOI : 10.1109/AFGR.2008.4813324

W. Zeng, Y. Zeng, Y. Wang, X. Yin, X. Gu et al., 3D Non-rigid Surface Matching and Registration Based on Holomorphic Differentials, Proc. European Conf. on Computer Vision, 2008.
DOI : 10.1007/978-3-540-88690-7_1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.4769

Y. Zeng, C. Wang, X. Gu, D. Samaras, and N. Paragios, A generic deformation model for dense nonrigid surface registration: a higher-order MRF-based approach, Proc. IEEE Int'l Conf. Computer Vision, pp.3360-3367, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00856323

Y. Zeng, C. Wang, Y. Wang, X. Gu, D. Samaras et al., Dense non-rigid surface registration using high-order graph matching, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.382-389, 2010.
DOI : 10.1109/CVPR.2010.5540189
URL : https://hal.archives-ouvertes.fr/hal-00856064

Y. Zeng, C. Wang, Y. Wang, X. Gu, D. Samaras et al., Intrinsic dense 3D surface tracking, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995513
URL : https://hal.archives-ouvertes.fr/hal-00856137

D. Zhang and M. Hebert, Harmonic maps and their applications in surface matching, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1999.

.. Mrf-formulation-for-shape-registration, 12 3.2.1 Deformation constraints, p.13