• search hit 7 of 87
Back to Result List

Unsupervised Description of 3D Shapes by Superquadrics Using Deep Learning

  • The decomposition of 3D shapes into simple yet representative components is a very intriguing topic in computer vision as it is very useful for many possible applications. Superquadrics may be used with benefit to obtain an implicit representation of the 3D shapes, as they allow to represent a wide range of possible forms by few parameters. However, in the computation of the shape representation, there is often an intricate trade-off between the variation of the represented geometric forms and the accuracy in such implicit approaches. In this paper, we propose an improved loss function, and we introduce beneficial computational techniques. By comparing results obtained by our new technique to the baseline method, we demonstrate that our results are more reliable and accurate, as well as much faster to obtain.

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author: Mahmoud Eltaher, Michael BreußGND
DOI:https://doi.org/10.1007/978-981-19-7867-8_9
ISBN:978-981-19-7866-1
ISSN:978-981-19-7867-8
Title of the source (English):Computer Vision and Machine Intelligence : Proceedings of CVMI 2022
Publisher:Springer
Place of publication:Singapore
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2023
Tag:Implicit shape representation Superquadrics Deep learning 3D shape description
First Page:95
Last Page:107
Series ; volume number:Lecture Notes in Networks and Systems book series (LNNS) ; volume 586
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Angewandte Mathematik
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.