Davide Boscaini
TitleCited byYear
Geometric deep learning on graphs and manifolds using mixture model cnns
F Monti, D Boscaini, J Masci, E Rodola, J Svoboda, MM Bronstein
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
4292017
Geodesic Convolutional Neural Networks on Riemannian Manifolds
J Masci, D Boscaini, MM Bronstein, P Vandergheynst
International IEEE Workshop on 3D Representation and Recognition (3dRR), 2015
3192015
Learning shape correspondence with anisotropic convolutional neural networks
D Boscaini, J Masci, E Rodolà, M Bronstein
Advances in neural information processing systems, 3189-3197, 2016
2262016
Learning class‐specific descriptors for deformable shapes using localized spectral convolutional networks
D Boscaini, J Masci, S Melzi, MM Bronstein, U Castellani, ...
Computer Graphics Forum 34 (5), 13-23, 2015
1372015
Anisotropic diffusion descriptors
D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers
Computer Graphics Forum 35 (2), 431-441, 2016
792016
Shapenet: Convolutional neural networks on non-euclidean manifolds
J Masci, D Boscaini, M Bronstein, P Vandergheynst
352015
Shape‐from‐operator: Recovering shapes from intrinsic operators
D Boscaini, D Eynard, D Kourounis, MM Bronstein
Computer Graphics Forum 34 (2), 265-274, 2015
312015
Geometric deep learning
J Masci, E Rodolà, D Boscaini, MM Bronstein, H Li
SIGGRAPH ASIA 2016 Courses, 1-50, 2016
242016
A sparse coding approach for local-to-global 3D shape description
D Boscaini, U Castellani
The Visual Computer, 2014
72014
Coulomb shapes: using electrostatic forces for deformation-invariant shape representation
D Boscaini, R Girdziusaz, MM Bronstein
Eurographics Workshop on 3D Object Retrieval (3DOR), 9-15, 2014
5*2014
Local signature quantization by sparse coding
D Boscaini, U Castellani
Eurographics Workshop on 3D Object Retrieval (3DOR), 9-16, 2013
22013
System and a method for learning features on geometric domains
M Bronstein, D Boscaini, J Masci, P Vandergheynst
US Patent 10,013,653, 2018
12018
Deep learning for shape analysis
M Bronstein, E Kalogerakis, E Rodola, J Masci, D Boscaini
Proceedings of the 37th Annual Conference of the European Association for …, 2016
12016
Shape-from-intrinsic operator
D Boscaini, D Eynard, MM Bronstein
arXiv preprint arXiv:1406.1925, 2014
12014
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
P Gainza, F Sverrisson, F Monti, E Rodolà, D Boscaini, MM Bronstein, ...
Nature Methods, 1-9, 2019
2019
Structured Domain Adaptation for 3D Keypoint Estimation
LO Vasconcelos, M Mancini, D Boscaini, B Caputo, E Ricci
2019 International Conference on 3D Vision (3DV), 57-66, 2019
2019
3D Shape Segmentation with Geometric Deep Learning
D Boscaini, F Poiesi
International Conference on Image Analysis and Processing, 454-465, 2019
2019
System and a method for learning features on geometric domains
M Bronstein, D Boscaini, F Monti
US Patent 10,210,430, 2019
2019
Learning interaction patterns from surface representations of protein structure.
P Gainza, F Sverrisson, F Monti, E Rodolà, D Boscaini, M Bronstein, ...
2019
Geometric Deep Learning for Shape Analysis
D Boscaini
Università della Svizzera italiana, 2017
2017
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Articles 1–20