This thesis combines Computer Aided Geometric Design with Deep Learning to develop geometric reverse engineering methods for data-driven free-form spline geometries. We focus on reconstructing CAD models from point clouds with varying configurations, from uniform to scattered and noisy. Central to this is the parameterization problem: mapping input data to a parametric domain. We propose data-driven parameterization methods based on geometric deep learning for both univariate and multivariate cases, achieving higher accuracy than standard methods. We also introduce adaptive fitting schemes combining moving parameterization with hierarchical B-splines, significantly enhancing model quality, also compared to state of the art reconstruction schemes.
Eindhoven University of Technology, Netherlands - ORCID: 0009-0003-9116-9978
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