Learning shape correspondence with anisotropic convolutional neural networks

Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein
Proc. NIPS, 2016
also available as technical report: arXiv:1605.06437, 2016

Anisotropic diffusion descriptors

Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers
Computer Graphics Forum (Proc. Eurographics), 2016

Geodesic convolutional neural networks on Riemannian manifolds

Jonathan Masci*, Davide Boscaini*, Michael M. Bronstein, Pierre Vandergheynst
* equal contribution
Proc. International IEEE Workshop on 3D Representation and Recognition (3dRR), 2015

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M. Bronstein, Umberto Castellani, Pierre Vandergheynst
Computer Graphics Forum (Proc. SGP), 34(5), pp. 13–23, 2015

ShapeNet: convolutional neural networks on non-Euclidean manifolds

Jonathan Masci*, Davide Boscaini*, Michael M. Bronstein, Pierre Vandergheynst
* equal contribution
arXiv:1501.06297, 2015

Shape-from-operator: recovering shapes from intrinsic operators

Davide Boscaini, Davide Eynard, Drosos Kourounis, Michael M. Bronstein
Computer Graphics Forum (Proc. Eurographics), 34(2), pp. 265–274, 2015

Shape-from-intrinsic operator

Davide Boscaini, Davide Eynard, Michael M. Bronstein
arXiv:1406.1925, 2014

Coulomb shapes: using electrostatic forces for deformation-invariant shape representation

Davide Boscaini, Ramunas Girdziusas, Michael M. Bronstein
Proc. Eurographics Workshop on 3D Object Retrieval (3DOR), pp. 9–15, 2014

A sparse coding approach for local-to-global 3D shape description

Davide Boscaini, Umberto Castellani
The Visual Computer, 30(11), pp. 1233–1245, 2014

Local signature quantization by sparse coding

Davide Boscaini, Umberto Castellani
Proc. Eurographics Workshop on 3D Object Retrieval (3DOR), pp. 9-16, 2013