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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Proceedings ArticleDOI
Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space
TL;DR: This work derives the proposed recovery technique capable of guaranteeing a bijective correspondence and producing significantly higher accuracy and smoothness from the statistical framework of kernel density estimation and demonstrates its performance on several challenging deformable 3D shape matching datasets.
Journal ArticleDOI
Fully Spectral Partial Shape Matching
Or Litany,Emanuele Rodolà,Alexander M. Bronstein,Alexander M. Bronstein,Michael M. Bronstein,Michael M. Bronstein +5 more
TL;DR: An efficient procedure for calculating partial dense intrinsic correspondence between deformable shapes performed entirely in the spectral domain is proposed and a variant of the JAD problem with an appropriately modified coupling term allows to construct quasi‐harmonic bases localized on the latent corresponding parts.
Proceedings ArticleDOI
RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience
TL;DR: This paper proposes a general purpose fiducial marker system that can be deemed to add some valuable features to the pack by exploiting the projective properties of a circular set of sizeable dots and proposes a detection algorithm that is highly accurate.
Journal ArticleDOI
A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes
TL;DR: An evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level is adopted, allowing us to attack a more challenging scenario where model and scene have different, unknown scales.
Proceedings ArticleDOI
Efficient Deformable Shape Correspondence via Kernel Matching
Matthias Vestner,Zorah Lähner,Amit Boyarski,Or Litany,Ron Slossberg,Tal Remez,Emanuele Rodolà,Alexander M. Bronstein,Michael M. Bronstein,Ron Kimmel,Daniel Cremers +10 more
TL;DR: In this article, a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality is presented. But the method is based on the difference of convex functions (DC) programming.