Parameterization for point cloud spline fitting
- Sofia Imperatore
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1002-7.07
In this Chapter, we propose different data-driven parameterization procedures depending on the nature of the input data, whether they consist of point sequences or point clouds, as well as whether they are organized or scattered. CNN are employed for the parameterization learning problem of points on a rectilinear grid; on the other hand, we propose to employ methods from geometric deep learning to properly address the parameterization learning problem for unstructured data configurations.
- Keywords:
- Data parameterization,
- Convolutional Neural Networks,
- Graph Convolutional neural networks,
- gridded data,
- scattered data,
Technical University of Eindhoven, Netherlands - ORCID: 0009-0003-9116-9978
Chapter Information
Chapter Title
Parameterization for point cloud spline fitting
Authors
Sofia Imperatore
Language
English
DOI
10.36253/979-12-215-1002-7.07
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