M. Abadi, A. Agarwal, and P. Barham, TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

Y. Aflalo, A. Dubrovina, and R. Kimmel, Spectral generalized multi-dimensional scaling, International Journal of Computer Vision, vol.118, issue.3, pp.380-392, 2016.

D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers et al., SCAPE: Shape Completion and Animation of People, In ACM Transactions on Graphics, vol.24, issue.6, pp.408-416, 2005.

M. Aubry, U. Schlickewei, and D. Cremers, The wave kernel signature: A quantum mechanical approach to shape analysis, vol.31, 2007.

S. Biasotti, A. Cerri, M. Bronstein, and . Bronstein, Recent trends, applications, and perspectives in 3d shape similarity assessment, Computer Graphics Forum, vol.35, pp.87-119, 2016.

F. Bogo, J. Romero, M. Loper, and M. J. Black, FAUST: Dataset and evaluation for 3D mesh registration, Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), vol.1, p.6, 2014.

D. Boscaini, J. Masci, S. Melzi, M. Michael, U. Bronstein et al., Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks, Computer Graphics Forum, vol.34, p.7, 2015.

D. Boscaini, J. Masci, E. Rodola, and M. M. Bronstein, Learning shape correspondence with anisotropic convolutional neural networks, Proc. NIPS, pp.3189-3197, 2016.

O. Burghard, A. Dieckmann, and R. Klein, Embedding shapes with Green's functions for global shape matching, Computers & Graphics, vol.68, issue.2, pp.1-10, 2017.

E. Corman, M. Ovsjanikov, and A. Chambolle, Supervised descriptor learning for nonrigid shape matching, Proc. ECCV Workshops (NORDIA), vol.2, p.3, 2014.

L. Cosmo, E. Rodola, J. Masci, A. Torsello, and M. M. Bronstein, Matching deformable objects in clutter, Fourth International Conference on, pp.1-10, 2016.

M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, Advances in Neural Information Processing Systems, pp.3844-3852, 2016.

J. Ba and D. P. Kingma, Adam: A method for stochastic optimization, ICLR, 2015.

D. Eynard, E. Rodola, K. Glashoff, and M. M. Bronstein, Coupled functional maps, 3D Vision (3DV), vol.5, p.7, 2004.

D. Ezuz and M. Ben-chen, Deblurring and denoising of maps between shapes, Computer Graphics Forum, vol.36, p.3, 2017.

O. Halimi and O. Litany, Emanuele Rodol'a, Alex Bronstein, and Ron Kimmel. Unsupervised learning of dense shape correspondence, CVPR, 2019.

Q. Huang, F. Wang, and L. Guibas, Functional map networks for analyzing and exploring large shape collections, ACM Transactions on Graphics (TOG), vol.33, issue.4, p.36, 2014.

R. Huang and M. Ovsjanikov, Adjoint map representation for shape analysis and matching, Computer Graphics Forum, vol.36, pp.151-163
URL : https://hal.archives-ouvertes.fr/hal-01741932

. Wiley-online-library, , 2017.

G. Vladimir, Y. Kim, T. Lipman, and . Funkhouser, Blended intrinsic maps, In ACM Transactions on Graphics, vol.30, issue.7, p.79, 2011.

A. Kovnatsky, M. Michael, X. Bronstein, P. Bresson, and . Vandergheynst, Functional correspondence by matrix completion, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.905-914, 2015.

A. Kovnatsky, M. Michael, A. M. Bronstein, K. Bronstein, R. Glashoff et al., Coupled quasi-harmonic bases, Computer Graphics Forum, vol.32, pp.439-448, 2013.

A. Kovnatsky, K. Glashoff, and M. M. Bronstein, MADMM: a generic algorithm for nonsmooth optimization on manifolds, Proc. ECCV, pp.680-696, 2016.

O. Litany, E. Remez, A. M. Rodolà, M. M. Bronstein, and . Bronstein, Deep functional maps: Structured prediction for dense shape correspondence, IEEE International Conference on Computer Vision (ICCV), vol.6, p.7, 2005.

O. Litany, E. Rodolà, A. M. Bronstein, and M. M. Bronstein, Fully spectral partial shape matching, Computer Graphics Forum, vol.36, pp.247-258, 2017.

R. Litman and . Alexander-m-bronstein, Learning spectral descriptors for deformable shape correspondence. IEEE transactions on pattern analysis and machine intelligence, vol.36, pp.171-180, 2014.

J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst, Geodesic convolutional neural networks on riemannian manifolds, Proceedings of the IEEE international conference on computer vision workshops, pp.37-45, 2006.

F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda et al., Geometric deep learning on graphs and manifolds using mixture model cnns, CVPR, vol.1, pp.5425-5434, 2017.

D. Nogneng, S. Melzi, E. Rodolà, U. Castellani, M. Bronstein et al., Improved functional mappings via product preservation, Computer Graphics Forum, vol.37, p.7, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01741750

D. Nogneng and M. Ovsjanikov, Informative descriptor preservation via commutativity for shape matching, Computer Graphics Forum, vol.36, issue.2, pp.259-267, 2005.

M. Ovsjanikov, M. Ben-chen, J. Solomon, A. Butscher, and L. Guibas, Functional Maps: A Flexible Representation of Maps Between Shapes, ACM Transactions on Graphics (TOG), vol.31, issue.4, p.7, 2005.

M. Ovsjanikov, E. Corman, M. Bronstein, E. Rodolà, M. Ben-chen et al., Computing and processing correspondences with functional maps, ACM SIGGRAPH 2017 Courses, SIG-GRAPH '17, vol.5, p.3, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01664767

K. B. Petersen and M. S. Pedersen, The matrix cookbook, 2012.

A. Poulenard and M. Ovsjanikov, Multidirectional geodesic neural networks via equivariant convolution, 2018.

A. Poulenard, P. Skraba, and M. Ovsjanikov, Topological function optimization for continuous shape matching, Computer Graphics Forum, vol.37, pp.13-25, 2018.

J. Ren, A. Poulenard, P. Wonka, and M. Ovsjanikov, Continuous and orientation-preserving correspondences via functional maps, ACM Transactions on Graphics (TOG), vol.37, issue.6, 2006.

E. Rodolà, L. Cosmo, M. Michael, A. Bronstein, D. Torsello et al., Partial functional correspondence, Computer Graphics Forum, vol.36, pp.222-236, 2017.

E. Rodolà, D. Moeller, and . Cremers, Point-wise map recovery and refinement from functional correspondence, Proc. Vision, Modeling and Visualization (VMV), 2015.

E. Rodolà, S. R. Bulo, T. Windheuser, M. Vestner, and D. Cremers, Dense non-rigid shape correspondence using random forests, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4177-4184, 2014.

S. Rosenberg, The Laplacian on a Riemannian manifold: an introduction to analysis on manifolds, vol.31, 1997.

R. Rustamov, M. Ovsjanikov, O. Azencot, M. Ben-chen, F. Chazal et al., Map-based exploration of intrinsic shape differences and variability, ACM Trans. Graphics, vol.32, issue.4, p.5, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00923609

R. Singh and J. Manhas, Composition Operators on Function Spaces, 1993.

W. Robert, J. Sumner, and . Popovi?, Deformation transfer for triangle meshes, In ACM Transactions on Graphics, vol.23, issue.1, pp.399-405, 2004.

J. Sun, M. Ovsjanikov, and L. Guibas, A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion, Computer graphics forum, vol.28, pp.1383-1392, 2009.

K. L. Gary, Z. Tam, Y. Cheng, . Lai, Y. Frank-c-langbein et al., Registration of 3d point clouds and meshes: a survey from rigid to nonrigid, IEEE transactions on visualization and computer graphics, vol.19, issue.7, pp.1199-1217, 2013.

F. Tombari, S. Salti, and L. D. Stefano, Unique signatures of histograms for local surface description, International Conference on Computer Vision (ICCV), vol.6, p.7, 2010.

H. Oliver-van-kaick, G. Zhang, D. Hamarneh, and . Cohen-or, A survey on shape correspondence, Computer Graphics Forum, vol.30, pp.1681-1707, 2011.

M. Vestner, Z. Lähner, and A. Boyarski,

A. Bronstein, M. Bronstein, R. Kimmel, and D. Cremers, Efficient deformable shape correspondence via kernel matching, Proc. 3DV, 2017.

M. Vestner, R. Litman, E. Rodolà, A. Bronstein, and D. Cremers, Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space, Proc. CVPR, pp.6681-6690, 2017.

L. Wang, A. Gehre, J. Michael-m-bronstein, and . Solomon, Kernel functional maps, Computer Graphics Forum, vol.37, pp.27-36, 2008.

Y. Wang, K. Liu, Y. Zhou, and . Tong, Vector field map representation for near conformal surface correspondence, Computer Graphics Forum, vol.37, pp.72-83, 2018.

L. Wei, Q. Huang, D. Ceylan, E. Vouga, and H. Li, Dense human body correspondences using convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.1, pp.1544-1553, 2016.

D. Yan, G. Bao, X. Zhang, and P. Wonka, Low-resolution remeshing using the localized restricted voronoi diagram, IEEE transactions on visualization and computer graphics, vol.20, issue.10, pp.1418-1427, 2014.