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
Chapter Information
Chapter Title
Introduction
Authors
Sofia Imperatore
Language
English
DOI
10.36253/979-12-215-1002-7.02
Peer Reviewed
Publication Year
2026
Copyright Information
© 2026 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
Adaptive spline approximation: data-driven parameterization and CAD model (re-)construction
Authors
Sofia Imperatore
Peer Reviewed
Number of Pages
196
Publication Year
2026
Copyright Information
© 2026 Author(s)
Content License
Metadata License
Publisher Name
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
Series Title
Premio Tesi di Dottorato Città di Firenze
Series ISSN
3103-3881
Series E-ISSN
3103-3989