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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.

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Metadaten
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):License LogoLizenzbedingungen IEEE