Deep Learning Parametrization for B-Spline Curve Approximation
Computational Geometry
2018-07-24 v1 Graphics
Abstract
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.
Cite
@article{arxiv.1807.08304,
title = {Deep Learning Parametrization for B-Spline Curve Approximation},
author = {Pascal Laube and Matthias O. Franz and Georg Umlauf},
journal= {arXiv preprint arXiv:1807.08304},
year = {2018}
}
Comments
Accepted at 3DV 2018