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
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