## Abstract

Finding the optimal parameterization for fitting a given sequence of data points with a parametric curve is a challenging problem that is equivalent to solving a highly non-linear system of equations. In this work, we propose the use of a residual neural network to approximate the function that assigns to a sequence of data points a suitable parameterization for fitting a polynomial curve of a fixed degree. Our model takes as an input a small fixed number of data points and the generalization to arbitrary data sequences is obtained by performing multiple evaluations. We show that the approach compares favorably to classical methods in a number of numerical experiments that include the parameterization of polynomial as well as non-polynomial data.

Original language | English |
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Article number | 101977 |

Journal | Computer Aided Geometric Design |

Volume | 85 |

DOIs | |

Publication status | Published - 2021 Feb |

## Keywords

- Curve fitting
- Deep learning
- Parameterization

## ASJC Scopus subject areas

- Modelling and Simulation
- Automotive Engineering
- Aerospace Engineering
- Computer Graphics and Computer-Aided Design