Abstract
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 language | English |
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Title of host publication | STAG 2021 - Smart Tools and Applications in Graphics, Eurographics Italian Chapter Conference |
Editors | Patrizio Frosini, Daniela Giorgi, Simone Melzi, Emanuele Rodola, Dieter Fellner |
Publisher | Eurographics Association |
Pages | 31-37 |
Number of pages | 7 |
ISBN (Electronic) | 9783038681656 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 8th Smart Tools and Applications in Graphics Conference, STAG 2021 - Virtual, Online Duration: 28 Oct 2021 → 29 Oct 2021 |
Publication series
Name | Eurographics Italian Chapter Proceedings - Smart Tools and Applications in Graphics, STAG |
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ISSN (Electronic) | 2617-4855 |
Conference
Conference | 8th Smart Tools and Applications in Graphics Conference, STAG 2021 |
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City | Virtual, Online |
Period | 28/10/21 → 29/10/21 |
Bibliographical note
Publisher Copyright:© 2021 The Author(s)
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Software