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Emanuele Rodolą
Emanuele Rodolą
Professor of Computer Science, Sapienza University of Rome
Verified email at di.uniroma1.it - Homepage
Title
Cited by
Cited by
Year
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
20132017
Learning shape correspondence with anisotropic convolutional neural networks
D Boscaini, J Masci, E Rodolą, M Bronstein
Advances in neural information processing systems 29, 2016
5802016
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
4712020
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
4542022
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
2732017
Partial functional correspondence
E Rodolą, L Cosmo, MM Bronstein, A Torsello, D Cremers
Computer graphics forum 36 (1), 222-236, 2017
2652017
Dense Non-Rigid Shape Correspondence using Random Forests
E Rodolą, S Rota Bulņ, T Windheuser, M Vestner, D Cremers
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014
1872014
RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience
F Bergamasco, A Albarelli, E Rodolą, A Torsello
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 113-120, 2011
1642011
Zoomout: Spectral upsampling for efficient shape correspondence
S Melzi, J Ren, E Rodola, A Sharma, P Wonka, M Ovsjanikov
arXiv preprint arXiv:1904.07865, 2019
1572019
Unsupervised learning of dense shape correspondence
O Halimi, O Litany, E Rodola, AM Bronstein, R Kimmel
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
1532019
Anisotropic diffusion descriptors
D Boscaini, J Masci, E Rodolą, MM Bronstein, D Cremers
Computer Graphics Forum 35 (2), 431-441, 2016
1392016
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
1292017
Computing and processing correspondences with functional maps
M Ovsjanikov, E Corman, M Bronstein, E Rodolą, M Ben-Chen, L Guibas, ...
SIGGRAPH ASIA 2016 Courses, 9, 2016
1292016
A scale independent selection process for 3d object recognition in cluttered scenes
E Rodola, A Albarelli, F Bergamasco, A Torsello
International journal of computer vision 102, 129-145, 2013
1272013
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
1262017
Multiview registration via graph diffusion of dual quaternions
A Torsello, E Rodolą, A Albarelli
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on …, 2011
1172011
Fully spectral partial shape matching
O Litany, E Rodolą, AM Bronstein, MM Bronstein
Computer Graphics Forum 36 (2), 247-258, 2017
1162017
A Game-Theoretic Approach to Deformable Shape Matching
E Rodolą, AM Bronstein, A Albarelli, F Bergamasco, A Torsello
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012
1012012
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
91*2017
Non‐rigid puzzles
O Litany, E Rodolą, AM Bronstein, MM Bronstein, D Cremers
Computer Graphics Forum 35 (5), 135-143, 2016
862016
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Articles 1–20