Subjective study of 3D mesh quality scores in virtual reality

Alfarasani, Dalia Ahmed, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2023. Subjective study of 3D mesh quality scores in virtual reality. Presented at: ICVARS 2023: 2023 the 7th International Conference on Virtual and Augmented Reality Simulations, Sydney, Australia, 3-5 March 2023. ICVARS '23: Proceedings of the 2023 7th International Conference on Virtual and Augmented Reality Simulations. ACM, pp. 7-13. 10.1145/3603421.3603423

Full text not available from this repository.

Abstract

3D meshes are widely used in Virtual Reality as essential graphical elements for creating immersive virtual environments. In practice, the 3D meshes being used are often subject to some manipulations, where some details may be lost and some noise could be introduced, e.g., due to the limited transmission bandwidth. While existing studies have considered 3D mesh quality measures in the desktop setting, we consider how different 3D distortion types affect the perceptual quality of 3D shapes when viewed in a Virtual Reality setup (with users wearing a Meta/Oculus headset). Our experiment collected mean opinion scores (MOS) for each distorted shape by showing both distorted and reference shapes to users, and compared the results with meshes viewed in the traditional display. This paper aims to understand the effect of different types and levels of 3D mesh distortions on perceived quality and user experience in VR. We analyse correlations of two settings (VR and desktop) using the Pearson and Spearman correlation coefficients, which show a positive relationship between the two settings. However, in virtual reality, perception appears more sensitive to particular distortions than others, compared with the desktop setting, which can provide helpful guidance for downstream applications.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://www.acm.org/publications/policies/copyright_policy#Background, Start Date: 2023-03-03
Publisher:	ACM
ISBN:	9781450397469
Last Modified:	01 Jul 2025 11:00
URI:	https://orca.cardiff.ac.uk/id/eprint/179453

Actions (repository staff only)

Edit Item