Deep Learning Parametrization for B-Spline Curve Approximation
- In this paper we present a method using deep learning to compute parametrizations for B-spline curve approximation. Existing methods consider the computation of parametric values and a knot vector as separate problems. We propose to train interdependent deep neural networks to predict parametric values and knots. We show that it is possible to include B-spline curve approximation directly into the neural network architecture. The resulting parametrizations yield tight approximations and are able to outperform state-of-the-art methods.
| Author: | Pascal Laube, Matthias O. FranzORCiDGND, Georg UmlaufORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1109/3DV.2018.00084 |
| ISSN: | 2475-7888 |
| Parent Title (English): | International Conference on 3D Vision (3DV), 5-8 Sept. 2018, Verona, Italy |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2018 |
| Release Date: | 2019/01/18 |
| First Page: | 691 |
| Last Page: | 699 |
| Note: | Volltextzugriff im Campusnetz der Hochschule Konstanz möglich (Datenbank IEEE Xplore) |
| Institutes: | Institut für Optische Systeme - IOS |
| Relevance: | Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband) |
| Open Access?: | Nein |
| Licence (English): |  Lizenzbedingungen IEEE |
