Parameterization for polynomial curve approximation via residual deep neural networks

DOI10.1016/j.cagd.2021.101977zbMath1467.65017OpenAlexW3137568919MaRDI QIDQ2020358

Felix Scholz, Bert Jüttler

Publication date: 23 April 2021

Published in: Computer Aided Geometric Design (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cagd.2021.101977

zbMATH Keywords

curve fittingcurve parameterizationdeep neural networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)

Related Items (2)

Boundary correspondence for iso-geometric analysis based on deep learning ⋮ GMP2021 - 15th international conference on geometric modeling and processing

Uses Software

• Adam

Cites Work

• Unnamed Item
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• A universal parametrization in B-spline curve and surface interpolation
• High quality local interpolation by composite parametric surfaces
• Optimal parametric interpolants of circular arcs
• Approximation of monotone clothoid segments by degree 7 Pythagorean-hodograph curves
• An improved Hoschek intrinsic parametrization
• Improving angular speed uniformity by reparameterization
• Parameterization for Curve Interpolation
• On geometric interpolation by planar parametric polynomial curves

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