Conclusion and future development
- Sofia Imperatore
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1002-7.10
This chapter concludes the thesis by summarizing the core contents and contributions and providing possible future research directions. Specifically, it reviews the integration of Computer Aided Geometric Design (CAGD) with Deep Learning (DL) to develop robust adaptive fitting schemes using THB-splines. The conclusion highlights the main thesis contributions, such as enhanced fitting via iteratively reweighted least squares and quasi-interpolation, data-driven parameterization through (graph) convolutional neural networks, and the design and development of the "moving parameterization" paradigm within adaptive (THB-)spline schemes. Finally, it outlines critical future research directions: employing DL for automatic boundary detection, developing quasi-conformal parameterizations to minimize geometric distortion, and extending the proposed methodologies to multi-patch frameworks for industrial CAD design.
- Keywords:
- Geometric Deep Learning,
- Computer Aided Geometric Design,
- boudary detection,
- multi-patch fitting,
- quasi-conformal parameterization,
Technical University of Eindhoven, Netherlands - ORCID: 0009-0003-9116-9978
Informazioni sul capitolo
Titolo del capitolo
Conclusion and future development
Autori
Sofia Imperatore
Lingua
Inglese
DOI
10.36253/979-12-215-1002-7.10
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