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EDBT 2026 Demo / reviewers in the wild / expert

Luca Di Angelo

dblp:84/3117 · DBLP profile ↗
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7ranked-venue papers
5as first author
0since 2021 · last 2019
0000-0002-5341-0500ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 6 · 4 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
5 papers
Geometric modeling and processing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › surface processing
surface segmentation		 0.622019
Fillets, rounds, grooves and sharp edges segmentation from 3D scanned surfaces · Comput. Aided Des. 2019
Geometric segmentation of 3D scanned surfaces · Comput. Aided Des. 2015
Geometric modeling and processing › mesh segmentation
feature segmentation		 0.412019
Fillets, rounds, grooves and sharp edges segmentation from 3D scanned surfaces · Comput. Aided Des. 2019
Geometric modeling and processing
surface reconstruction		 0.112011
A new mesh-growing algorithm for fast surface reconstruction · Comput. Aided Des. 2011
Geometric modeling and processing › shape representation › mesh representation
triangle mesh		 0.112010
C1 continuities detection in triangular meshes · Comput. Aided Des. 2010
Geometric modeling and processing
feature recognition		 0.012004
An approach for feature semantics recognition in geometric models · Comput. Aided Des. 2004

Methods — techniques the papers use, named apart from their topics

mesh-growing algorithm · 0.1geometric model analysis · 0.0
YearPublicationVenuePosition
2019 Fillets, rounds, grooves and sharp edges segmentation from 3D scanned surfaces
Luca Di Angelo, Paolo Di Stefano, Anna Eva Morabito
 Comput. Aided Des.1
2018 Axis estimation of thin-walled axially symmetric solids
Luca Di Angelo, Paolo Di Stefano
 Pattern Recognit. Lett.1
2015 Geometric segmentation of 3D scanned surfaces
Luca Di Angelo, Paolo Di Stefano
 Comput. Aided Des.1
2011 A new mesh-growing algorithm for fast surface reconstruction
Luca Di Angelo, Paolo Di Stefano, Luigi Giaccari
 Comput. Aided Des.1
2010 C1 continuities detection in triangular meshes
Luca Di Angelo, Paolo Di Stefano
 Comput. Aided Des.1
2004 An approach for feature semantics recognition in geometric models
Paolo Di Stefano, Francesco Bianconi, Luca Di Angelo
 Comput. Aided Des.3
2003 Neural Network Based Geometric Primitive for Airfoil Design
abstract
A geometric primitive for CAD implementation is presented (Bezier neural network, BNN). It is specifically designed to reproduce geometric shapes with functional requirements such as aerodynamic and hydrodynamic profiles. This primitive can be useful when a known and well defined map between functional requirements and geometric data does not exist, and it have to be deduced by a physical or numerical experimental analysis. BNN gives rise to a typical CAD representation, a Bezier curve, of a functional profile, once the functional parameters are supplied. In BNN the capability of neural network to approximate very complex and non-linear function has been combined with the capability of Bezier functions to describe geometry, in a unique neural network. In this work BNN is used in the representation of aerodynamic profiles starting to their typical functional parameters: lift and drag coefficients, Reynolds number and angle of attack. BNN is tested in reproducing the wing profile of the 4-digit NACA series. The output of BNN is compared with the results of a fluid-dynamic analysis performed by commercial software.
Paolo Di Stefano, Luca Di Angelo
 Shape Modeling International2