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Exploiting 3D Variational Autoencoders for Interactive Vehicle Design

Published online by Cambridge University Press:  26 May 2022

S. Saha ,
L. L. Minku ,
X. Yao ,
B. Sendhoff  and
S. Menzel
S. Saha*
Affiliation:
Honda Research Institute Europe GmbH, Germany University of Birmingham, United Kingdom
L. L. Minku
Affiliation:
University of Birmingham, United Kingdom
X. Yao
Affiliation:
University of Birmingham, United Kingdom Southern University of Science and Technology, China
B. Sendhoff
Affiliation:
Honda Research Institute Europe GmbH, Germany
S. Menzel
Affiliation:
Honda Research Institute Europe GmbH, Germany
*
sneha.saha@honda-ri.de

Abstract

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In automotive digital development, 3D prototype creation is a team effort of designers and engineers, each contributing with ideas and technical evaluations through means of computer simulations. To support the team in the 3D design ideation and exploration task, we propose an interactive design system for assisted design explorations and faster performance estimations. We utilize the advantage of deep learning-based autoencoders to create a low-dimensional latent manifold of 3D designs, which is utilized within an interactive user interface to guide and strengthen the decision-making process.

Keywords

data-driven designcollaborative design3D modelling
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1747 - 1756
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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