E
Emanuele Rodolà
Researcher at Sapienza University of Rome
Publications - 151
Citations - 7272
Emanuele Rodolà is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Shape analysis (digital geometry) & Computer science. The author has an hindex of 34, co-authored 120 publications receiving 5133 citations. Previous affiliations of Emanuele Rodolà include University of Tokyo & University of Lugano.
Papers
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Proceedings ArticleDOI
Shape Correspondence with Isometric and Non-Isometric Deformations
Roberto M. Dyke,Chris Stride,Yu-Kun Lai,Paul L. Rosin,Mathieu Aubry,Amit Boyarski,Alexander M. Bronstein,Michael M. Bronstein,Daniel Cremers,Matthew Fisher,Thibault Groueix,Daoliang Guo,Vladimir G. Kim,Ron Kimmel,Zorah Lähner,Kun Li,Or Litany,Tal Remez,Emanuele Rodolà,Bryan Russell,Yusuf Sahillioglu,Ron Slossberg,Gary K. L. Tam,Matthias Vestner,Zhipei Wu,Jingyu Yang +25 more
Journal ArticleDOI
Orthogonalized Fourier Polynomials for Signal Approximation and Transfer
Filippo Maggioli,Simone Melzi,Maksim Ovsjanikov,Michael M. Bronstein,Michael M. Bronstein,Emanuele Rodolà +5 more
TL;DR: The proposed solution is based on taking pointwise polynomials of the Fourier‐like Laplacian eigenbasis, which provides a compact and expressive representation for general signals defined on the surface, which is more robust to discretization artifacts, deformation and noise as compared to alternative approaches.
Posted Content
Functional Maps Representation on Product Manifolds
TL;DR: In this article, the authors model maps as densities over the product manifold of the input shapes; these densities can be treated as scalar functions and therefore are manipulable using the language of signal processing on manifolds.
Journal ArticleDOI
A parametric analysis of discrete Hamiltonian functional maps
TL;DR: An in‐depth theoretical investigation of the discrete Hamiltonian eigenbasis, which remains quite unexplored in the geometry processing community, and exploits the Hamiltonian‐Dirichlet connection in a partial shape matching problem.
Journal ArticleDOI
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Antonio Norelli,Marco Fumero,Valentino Maiorca,Luca Moschella,Emanuele Rodolà,Francesco Locatello +5 more
TL;DR: It is shown that sparse relative representations arecient to align text and images without training any network, and represents a simple yet surprisingly strong baseline for foundation multimodal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.