| 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 | 2312 | 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 | 925 | 2015 |
| 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 | 664 | 2020 |
| Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in neural information processing systems 29, 2016 | 623 | 2016 |
| 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 | 254 | 2015 |
| Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016 | 149 | 2016 |
| 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 | 75* | 2022 |
| Shapenet: Convolutional neural networks on non-euclidean manifolds J Masci, D Boscaini, M Bronstein, P Vandergheynst | 69 | 2015 |
| Geometric deep learning J Masci, E Rodolà, D Boscaini, MM Bronstein, H Li SIGGRAPH ASIA 2016 Courses, 1-50, 2016 | 57 | 2016 |
| Distinctive 3D local deep descriptors F Poiesi, D Boscaini 2020 25th International conference on pattern recognition (ICPR), 5720-5727, 2021 | 55 | 2021 |
| 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 |
| 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 | 39 | 2021 |
| 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 | 22 | 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 | 14 | 2020 |
| Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection ML Mekhalfi, D Boscaini, F Poiesi arXiv preprint arXiv:2308.15353, 2023 | 7 | 2023 |
| Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation J Corsetti, D Boscaini, F Poiesi Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 7 | 2023 |
| The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios L Riz, A Caraffa, M Bortolon, ML Mekhalfi, D Boscaini, A Moura, J Antunes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 7 | 2023 |
| 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 | 6* | 2014 |
| A sparse coding approach for local-to-global 3D shape description D Boscaini, U Castellani The Visual Computer, 2014 | 6 | 2014 |
Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRAVerified email at cs.ox.ac.uk
