2018/07/22 by Pascal Laube, Laube, Pascal, Matthias Franz +3
Engineering · #Advanced Numerical Analysis Techniques #Computational Geometry (cs.CG) #FOS: Computer and information sciences #Graphics (cs.GR)
paper · pdf · doi:10.48550/arxiv.1807.08304
openalex publication_date 2018/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.