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D-Index & Metrics

Computer Science

D-Index
41
Citations
7441
World Ranking
8827
National Ranking
246

Overview

Emanuele Rodolà is affiliated with Sapienza University of Rome in Italy. Their research spans primarily the field of Computer Science, with a focus on multiple subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Computer Graphics and Computer-Aided Design, and Signal Processing.

Their work covers a broad range of topics, prominently in 3D Shape Modeling and Analysis, Computer Graphics and Visualization Techniques, Human Pose and Action Recognition, Music and Audio Processing, Advanced Vision and Imaging, Speech and Audio Processing, and Speech Recognition and Synthesis.

Frequent coauthors collaborating with them include Simone Melzi, Luca Cosmo, Marco Fumero, Luca Moschella, and Emilian Postolache.

Rodolà has contributed to many publication venues, with notable frequent appearances in:

  • arXiv (Cornell University)
  • Computer Graphics Forum
  • International Journal of Computer Vision
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Eurographics

Among recent papers authored or co-authored by Emanuele Rodolà are:

  • Cluster-driven graph federated learning over multiple domains (2021) - Institutional Research Information System (Università degli Studi di Trento)
  • FARM: Functional Automatic Registration Method for 3D Human Bodies (2020) - BOA (University of Milano-Bicocca)
  • Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification (2021) - IEEE Transactions on Circuits and Systems for Video Technology
  • Spectral Shape Recovery and Analysis Via Data-driven Connections (2021) - International Journal of Computer Vision
  • Wavelet-based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis (2020) - Computer Graphics Forum

Best Publications

  • Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

    Federico Monti;Davide Boscaini;Jonathan Masci;Emanuele Rodola

  • Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.

    P. Gainza;F. Sverrisson;F. Monti;E. Rodolà

  • Learning shape correspondence with anisotropic convolutional neural networks

    Davide Boscaini;Jonathan Masci;Emanuele Rodolà;Michael M. Bronstein

  • Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

    Or Litany;Tal Remez;Emanuele Rodola;Alex Bronstein

  • Unsupervised Learning of Dense Shape Correspondence

    Oshri Halimi;Or Litany;Emanuele Rodola Rodola;Alex M. Bronstein

  • Dense Non-rigid Shape Correspondence Using Random Forests

    Emanuele Rodolà;Samuel Rota Bulò;Thomas Windheuser;Matthias Vestner

  • ZoomOut: spectral upsampling for efficient shape correspondence

    Simone Melzi;Jing Ren;Emanuele Rodolà;Abhishek Sharma

  • RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience

    Filippo Bergamasco;Andrea Albarelli;Emanuele Rodola;Andrea Torsello

  • Anisotropic diffusion descriptors

    D. Boscaini;J. Masci;E. Rodolà;M. M. Bronstein

  • A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes

    Emanuele Rodolà;Andrea Albarelli;Filippo Bergamasco;Andrea Torsello

  • Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space

    Matthias Vestner;Roee Litman;Emanuele Rodola;Alex Bronstein

  • Multiview registration via graph diffusion of dual quaternions

    Andrea Torsello;Emanuele Rodola;Andrea Albarelli

  • Fully Spectral Partial Shape Matching

    Or Litany;Emanuele Rodolà;Alexander M. Bronstein;Alexander M. Bronstein;Michael M. Bronstein;Michael M. Bronstein

  • Computing and processing correspondences with functional maps

    Maks Ovsjanikov;Etienne Corman;Michael Bronstein;Emanuele Rodolà

  • A game-theoretic approach to deformable shape matching

    Emanuele Rodola;Alex M. Bronstein;Andrea Albarelli;Filippo Bergamasco

  • Efficient Deformable Shape Correspondence via Kernel Matching

    Matthias Vestner;Zorah Lahner;Amit Boyarski;Or Litany

  • An Accurate and Robust Artificial Marker Based on Cyclic Codes

    Filippo Bergamasco;Andrea Albarelli;Luca Cosmo;Emanuele Rodola

  • Robust Camera Calibration using Inaccurate Targets

    Andrea Albarelli;Emanuele Rodolà;Andrea Torsello

  • Cluster-driven Graph Federated Learning over Multiple Domains

    Debora Caldarola;Massimiliano Mancini;Fabio Galasso;Marco Ciccone

  • 2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs

    Danilo Avola;Marco Cascio;Luigi Cinque;Gian Luca Foresti

  • Geometric deep learning

    Jonathan Masci;Emanuele Rodolà;Davide Boscaini;Michael M. Bronstein

  • Efficient Deformable Shape Correspondence via Kernel Matching

    Zorah Lähner;Matthias Vestner;Amit Boyarski;Or Litany

Frequent Co-Authors

Michael M. Bronstein
Michael M. Bronstein University of Oxford
Daniel Cremers
Daniel Cremers Technical University of Munich
Andrea Torsello
Andrea Torsello Ca Foscari University of Venice
Alexander M. Bronstein
Alexander M. Bronstein Technion – Israel Institute of Technology
Umberto Castellani
Umberto Castellani University of Verona
Maks Ovsjanikov
Maks Ovsjanikov École Polytechnique
Ron Kimmel
Ron Kimmel Technion – Israel Institute of Technology
Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Samuel Rota Bulò
Samuel Rota Bulò Facebook (United States)
Tatsuya Harada
Tatsuya Harada University of Tokyo

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