Lecture Notes
The full set of slides is available for download here:
Part 1: download (129 MB)
Part 2: download (8 MB)
Here are some useful references to the intrinsic constructions considered in the tutorial:
Learning shape correspondence with anisotropic convolutional neural networks
D. Boscaini, J. Masci, E. Rodola, M. Bronstein
arXiv 1605.06437 - Accepted to NIPS 2016
Matching deformable objects in clutter
L. Cosmo, E. Rodola, J. Masci, A. Torsello, M. Bronstein
Proc. 3DV 2016
Anisotropic diffusion descriptors
D. Boscaini, J. Masci, E. Rodola, M. Bronstein, D. Cremers
CGF 35(2), 2016 - Proc. Eurographics 2016
Geodesic convolutional neural networks on Riemannian manifolds
J. Masci, D. Boscaini, M. Bronstein, P. Vandergheynst
Proc. 3dRR 2015
D. Boscaini, J. Masci, S. Melzi, M. Bronstein, U. Castellani, P. Vandergheynst
CGF 34(5), 2015 - Proc. SGP 2015
Dense non-rigid shape correspondence using random forests
E. Rodola, S. Rota Bulo, T. Windheuser, M. Vestner, D. Cremers
Proc. CVPR 2014
Learning spectral descriptors for deformable shape correspondence
R. Litman, A. Bronstein
TPAMI 36(1), 2013