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.
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Journal ArticleDOI
Functional Maps Representation On Product Manifolds
Emanuele Rodolà,Zorah Lähner,Alexander M. Bronstein,Alexander M. Bronstein,Michael M. Bronstein,Michael M. Bronstein,Justin Solomon +6 more
TL;DR: This work discretizes product manifolds and their Laplace–Beltrami operators, and introduces localized spectral analysis of the product manifold as a novel tool for map processing.
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Point-wise Map Recovery and Refinement from Functional Correspondence
TL;DR: In this paper, the problem of point-wise map recovery from arbitrary functional maps has been studied and an efficient recovery process based on a simple probabilistic model has been devised, which achieves remarkable accuracy improvements in very challenging cases.
Proceedings ArticleDOI
Spatial Maps: From Low Rank Spectral to Sparse Spatial Functional Representations
TL;DR: A novel approach to the computation of a continuous bijective map between two surfaces moving from the low rank spectral representation to a sparse spatial representation, which induces a sub-vertex correspondence between the surfaces, but also the transportation of the whole surface, and thus the bijectivity of the induced map is assured.
Proceedings ArticleDOI
Universal Spectral Adversarial Attacks for Deformable Shapes
TL;DR: In this article, the existence of universal perturbations for geometric data (shapes) is demonstrated. But the attacks take the form of small perturbation to short eigenvalue sequences; the resulting geometry is then synthesized via shape-from-spectrum recovery.
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Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space
TL;DR: In this article, the authors proposed an alternative recovery technique capable of guaranteeing a bijective correspondence and producing significantly higher accuracy and smoothness, which does not depend on the assumption that the analyzed shapes are isometric.