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OPTIMISED MODELS FOR AR/VR BY USING GEOMETRIC COMPLEXITY METRICS TO CONTROL TESSELLATION

Published online by Cambridge University Press:  19 June 2023

Maximilian Peter Dammann ,
Wolfgang Steger  and
Kristin Paetzold-Byhain
Maximilian Peter Dammann*
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
Wolfgang Steger
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
Kristin Paetzold-Byhain
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
*
Dammann, Maximilian Peter, Technische Universität Dresden, Germany, maximilian_peter.dammann@tu-dresden.de

Abstract

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AR/VR applications are a valuable tool in product design and lifecycle. But the integration of AR/VR is not seamless, as CAD models need to be prepared for the AR/VR applications. One necessary data transformation is the tessellation of the analytically described geometry. To ensure the usability, visual quality and evaluability of the AR/VR application, time consuming optimisation is needed depending on the product complexity and the performance of the target device.

Widespread approaches to this problem are based on iterative mesh decimation. This approach ignores the varying importance of geometries and the required visual quality in engineering applications. Our predictive approach is an alternative that enables optimisation without iterative process steps on the tessellated geometry.

The contribution presents an approach that uses surface-based prediction and enables predictions of the perceived visual quality of the geometries. This contains the investigation of different geometric complexity metrics gathered from literature as basis for prediction models. The approach is implemented in a geometry preparation tool and the results are compared with other approaches.

Keywords

Virtual realityVisualisationMachine learningOptimisation
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2855 - 2864
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), 2023. Published by Cambridge University Press

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