Exploiting 3D Variational Autoencoders for Interactive Vehicle Design. (May 2022)

• Record Type: Journal Article
• Authors: Saha, S.; Minku, L. L.; Yao, X.; Sendhoff, B.; Menzel, S.
• Is Part Of: Proceedings of the Design Society. Volume 2(2022)
• Page Start: 1747
• Page End: 1756
• Publication Date: 2022-05
• Subjects: data-driven design -- collaborative design -- 3D modelling; Industrial design -- Congresses; Engineering design -- Congresses; 620.0042
• DOI: 10.1017/pds.2022.177
• Languages: English
• ISSNs: 2633-7762

Abstract

Abstract: 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.