| 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 | 2001 | 2017 |
| 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 | 835 | 2015 |
| Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in neural information processing systems 29, 2016 | 580 | 2016 |
| Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning P Gainza, F Sverrisson, F Monti, E Rodola, D Boscaini, MM Bronstein, ... Nature Methods 17 (2), 184-192, 2020 | 465 | 2020 |
| 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 | 237 | 2015 |
| Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016 | 139 | 2016 |
| Shapenet: Convolutional neural networks on non-euclidean manifolds J Masci, D Boscaini, M Bronstein, P Vandergheynst | 65 | 2015 |
| Shape‐from‐operator: Recovering shapes from intrinsic operators D Boscaini, D Eynard, D Kourounis, MM Bronstein Computer Graphics Forum 34 (2), 265-274, 2015 | 50 | 2015 |
| Geometric deep learning J Masci, E Rodolà, D Boscaini, MM Bronstein, H Li SIGGRAPH ASIA 2016 Courses, 1-50, 2016 | 45 | 2016 |
| Distinctive 3D local deep descriptors F Poiesi, D Boscaini 2020 25th International conference on pattern recognition (ICPR), 5720-5727, 2021 | 30 | 2021 |
| Joint supervised and self-supervised learning for 3d real world challenges A Alliegro, D Boscaini, T Tommasi 2020 25th International Conference on Pattern Recognition (ICPR), 6718-6725, 2021 | 30 | 2021 |
| Learning general and distinctive 3D local deep descriptors for point cloud registration F Poiesi, D Boscaini IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 3979-3985, 2022 | 23 | 2022 |
| Tractogram filtering of anatomically non-plausible fibers with geometric deep learning P Astolfi, R Verhagen, L Petit, E Olivetti, J Masci, D Boscaini, P Avesani Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 16 | 2020 |
| Generalisable and distinctive 3D local deep descriptors for point cloud registration F Poiesi, D Boscaini arXiv preprint arXiv:2105.10382, 1-12, 2021 | 10 | 2021 |
| System and a method for learning features on geometric domains M Bronstein, D Boscaini, J Masci, P Vandergheynst US Patent 10,013,653, 2018 | 7 | 2018 |
| 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 |
| A sparse coding approach for local-to-global 3D shape description D Boscaini, U Castellani The Visual Computer, 2014 | 5 | 2014 |
| Novel-view human action synthesis MI Lakhal, D Boscaini, F Poiesi, O Lanz, A Cavallaro Proceedings of the Asian Conference on Computer Vision, 2020 | 4 | 2020 |
| Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition J Svoboda, P Astolfi, D Boscaini, J Masci, MM Bronstein Proceedings of the IEEE International Joint Conference on Biometrics, 2020 | 3 | 2020 |
| Shape-from-intrinsic operator D Boscaini, D Eynard, MM Bronstein arXiv preprint arXiv:1406.1925, 2014 | 3 | 2014 |
Michael BronsteinDeepMind Professor of AI, University of OxfordVerified email at cs.ox.ac.uk
