Introduction
- Sofia Imperatore
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1002-7.02
This chapter illustrates fundamental concepts, the core research problem, and the contributions of the thesis. It presents the thesis methodologial unified framework of Computer Aided Geometric Design (CAGD) and Deep Learning (DL) and address geometric data approximation problem. Subsequenlty, to resolve the core challenges of data parameterization and approximant design, Truncated Hierarchical B-splines (THB-splines) are introduced together with Convolutional Neural Network (CNN) and Graph Convolutional neural Network (GCN) architectures. Finally, an overview of the novel contributions developed in the following chapters is provided: robust adaptive fitting via reweighted least squares and quasi-interpolation, data-driven parameterization, and the establishment of the moving parameterization paradigm.
- Keywords:
- Computer Aided Geometric Design,
- Geometric Deep Learning,
- data fitting,
- THB-splines,
- moving parameterization,
Technical University of Eindhoven, Netherlands - ORCID: 0009-0003-9116-9978
Informazioni sul capitolo
Titolo del capitolo
Introduction
Autori
Sofia Imperatore
Lingua
Inglese
DOI
10.36253/979-12-215-1002-7.02
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Adaptive spline approximation: data-driven parameterization and CAD model (re-)construction
Autori
Sofia Imperatore
Opera sottoposta a peer review
Numero di pagine
196
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1002-7
ISBN Print
979-12-215-1001-0
eISBN (pdf)
979-12-215-1002-7
eISBN (xml)
979-12-215-1003-4
Collana
Premio Tesi di Dottorato Città di Firenze
ISSN della collana
3103-3881
e-ISSN della collana
3103-3989