Emanuele Rodolà

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Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRAVerified email at cs.ox.ac.uk
Andrea TORSELLOProfessor of Computer Science, Università Ca' Foscari VeneziaVerified email at unive.it
Daniel CremersTechnical University of MunichVerified email at tum.de
Luca CosmoCa' Foscari University of VeniceVerified email at unive.it
Simone MelziAssociate Professor, Computer Science, University of Milano-BicoccaVerified email at unimib.it
Andrea AlbarelliUniversità Ca' Foscari di VeneziaVerified email at albarelli.it
Alex BronsteinDan Broida Academic Chair, Schmidt Chair of AI, CS@TechnionVerified email at technion.ac.il
Davide BoscainiResearcher, Fondazione Bruno KesslerVerified email at fbk.eu
Or LitanyTechnion, NVIDIAVerified email at nvidia.com
Maks OvsjanikovEcole Polytechnique, Google DeepMindVerified email at lix.polytechnique.fr
Jonathan MasciNNAISENSE SAVerified email at nnaisense.com
Riccardo MarinTechnical University of MunichVerified email at tum.de
Federico MontiUniversità della Svizzera italianaVerified email at usi.ch
Luca MoschellaAppleVerified email at apple.com
Marco FumeroPostDoctoral Researcher, Institute of Science and Technology Austria (ISTA)Verified email at ist.ac.at
Valentino MaiorcaEllis Ph.D. Student. Sapienza University of Rome & ISTAVerified email at di.uniroma1.it
Umberto CastellaniDipartimento di Informatica, University of VeronaVerified email at univr.it
Filippo BERGAMASCOUniversità Ca'Foscari di VeneziaVerified email at unive.it
Matthias VestnerComputer Vision Group, Technische Universität MünchenVerified email at in.tum.de
Arianna RampiniPhD Student, Computer Science, Sapienza University of RomeVerified email at di.uniroma1.it

Emanuele Rodolà

Professor of Computer Science, Sapienza University of Rome

Verified email at di.uniroma1.it - Homepage

Machine Learning Audio Geometric Deep Learning Geometry Processing Computer Vision


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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	2373	2017
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	1298	2022
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	707	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	629	2016
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	334	2017
Partial functional correspondence E Rodolà, L Cosmo, MM Bronstein, A Torsello, D Cremers Computer graphics forum 36 (1), 222-236, 2017	296	2017
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	208	2019
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	193	2014
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	192	2019
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	191	2011
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	152	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	152	2016
Anisotropic diffusion descriptors D Boscaini, J Masci, E Rodolà, MM Bronstein, D Cremers Computer Graphics Forum 35 (2), 431-441, 2016	149	2016
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	141	2017
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	133	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	132	2011
Fully spectral partial shape matching O Litany, E Rodolà, AM Bronstein, MM Bronstein Computer Graphics Forum 36 (2), 247-258, 2017	128	2017
Shrec 2019: Matching humans with different connectivity S Melzi, R Marin, E Rodolà, U Castellani, J Ren, A Poulenard, P Wonka, ... Eurographics Workshop on 3D Object Retrieval 7, 3, 2019	105	2019
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	105*	2017
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	105	2012

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