| 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 | 834 | 2017 |
| Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodoià, M Bronstein Proceedings of the 30th International Conference on Neural Information …, 2016 | 351 | 2016 |
| Dense Non-Rigid Shape Correspondence using Random Forests E Rodolà, S Rota Bulò, T Windheuser, M Vestner, D Cremers Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014 | 149 | 2014 |
| Partial functional correspondence E Rodolà, L Cosmo, MM Bronstein, A Torsello, D Cremers Computer Graphics Forum 36 (1), 222-236, 2017 | 148 | 2017 |
| Deep functional maps: Structured prediction for dense shape correspondence O Litany, T Remez, E Rodola, A Bronstein, M Bronstein Proceedings of the IEEE international conference on computer vision, 5659-5667, 2017 | 118 | 2017 |
| Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016 | 111 | 2016 |
| RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience F Bergamasco, A Albarelli, E Rodolà, A Torsello Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 113-120, 2011 | 107 | 2011 |
| A scale independent selection process for 3d object recognition in cluttered scenes E Rodolà, A Albarelli, F Bergamasco, A Torsello International journal of computer vision 102 (1), 129-145, 2013 | 101 | 2013 |
| Multiview registration via graph diffusion of dual quaternions A Torsello, E Rodolà, A Albarelli Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on …, 2011 | 92 | 2011 |
| A Game-Theoretic Approach to Deformable Shape Matching E Rodolà, AM Bronstein, A Albarelli, F Bergamasco, A Torsello Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012 | 89 | 2012 |
| Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space M Vestner, R Litman, E Rodola, A Bronstein, D Cremers Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 83 | 2017 |
| Imposing Semi-Local Geometric Constraints for Accurate Correspondences Selection in Structure from Motion: A Game-Theoretic Perspective A Albarelli, E Rodolà, A Torsello International journal of computer vision 97 (1), 36-53, 2012 | 68 | 2012 |
| Robust Camera Calibration using Inaccurate Targets A Albarelli, E Rodolà, A Torsello 21st British Machine Vision Conference 31 (2), 2010 | 63 | 2010 |
| 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 | 62 | 2020 |
| Computing and processing correspondences with functional maps M Ovsjanikov, E Corman, M Bronstein, E Rodola, M Ben-Chen, L Guibas, ... ACM SIGGRAPH 2017 Courses, 1-62, 2017 | 62 | 2017 |
| Computing and processing correspondences with functional maps M Ovsjanikov, E Corman, M Bronstein, E Rodolà, M Ben-Chen, L Guibas, ... SIGGRAPH ASIA 2016 Courses, 9, 2016 | 62 | 2016 |
| Fully spectral partial shape matching O Litany, E Rodolà, AM Bronstein, MM Bronstein Computer Graphics Forum 36 (2), 247-258, 2017 | 61 | 2017 |
| Non‐rigid puzzles O Litany, E Rodolà, AM Bronstein, MM Bronstein, D Cremers Computer Graphics Forum 35 (5), 135-143, 2016 | 58 | 2016 |
| Elastic net constraints for shape matching E Rodola, A Torsello, T Harada, Y Kuniyoshi, D Cremers Proceedings of the IEEE international conference on computer vision, 1169-1176, 2013 | 55 | 2013 |
| Efficient deformable shape correspondence via kernel matching M Vestner, Z Lähner, A Boyarski, O Litany, R Slossberg, T Remez, ... 2017 International Conference on 3D Vision (3DV), 517-526, 2017 | 53* | 2017 |
Michael BronsteinImperial College London / Twitter / University of LuganoVerified email at imperial.ac.uk
