LengthNet: Length Learning for Planar Euclidean Curves

Barak Or, Ido Amos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this work, we used a deep learning (DL) model to solve a fundamental problem in differential geometry. One can find many closed-form expressions for calculating curvature, length, and other geometric properties in the literature. As we know these properties, we are highly motivated to reconstruct them by using DL models. In this framework, our goal is to learn geometric properties from many examples. The simplest geometric object is a curve, and one of the fundamental properties is the length. Therefore, this work focuses on learning the length of planar sampled curves created by a simulation. The fundamental length axioms were reconstructed using a supervised learning approach. Following these axioms, a DL-based model, we named LengthNet, was established. For simplicity, we focus on the planar Euclidean curves.

Original languageEnglish
Title of host publicationSTAG 2021 - Smart Tools and Applications in Graphics, Eurographics Italian Chapter Conference
EditorsPatrizio Frosini, Daniela Giorgi, Simone Melzi, Emanuele Rodola, Dieter Fellner
PublisherEurographics Association
Number of pages7
ISBN (Electronic)9783038681656
StatePublished - 2021
Externally publishedYes
Event8th Smart Tools and Applications in Graphics Conference, STAG 2021 - Virtual, Online
Duration: 28 Oct 202129 Oct 2021

Publication series

NameEurographics Italian Chapter Proceedings - Smart Tools and Applications in Graphics, STAG
ISSN (Electronic)2617-4855


Conference8th Smart Tools and Applications in Graphics Conference, STAG 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 The Author(s)

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Software


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