## 抄録

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.

本文言語 | English |
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論文番号 | 101977 |

ジャーナル | Computer Aided Geometric Design |

巻 | 85 |

DOI | |

出版ステータス | Published - 2021 2 |

外部発表 | はい |

## ASJC Scopus subject areas

- モデリングとシミュレーション
- 自動車工学
- 航空宇宙工学
- コンピュータ グラフィックスおよびコンピュータ支援設計