Perceptual crack detection for rendered 3D textured meshes

Shafiee Sarvestani, Armin, Zhou, Wei and Wang, Zhou 2024. Perceptual crack detection for rendered 3D textured meshes. Presented at: IEEE International Conference on Quality of Multimedia Experience (QoMEX), Karlshamn, Sweden, 18 - 20 June 2024. Proceedings of 16th International Conference on Quality of Multimedia Experience (QoMEX). IEEE, pp. 1-7. 10.1109/QoMEX61742.2024.10598253

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Abstract

Recent years have witnessed many advancements in the applications of 3D textured meshes. As the demand continues to rise, evaluating the perceptual quality of this new type of media content becomes crucial for quality assurance and optimization purposes. Different from traditional image quality assessment, crack is an annoying artifact specific to rendered 3D meshes that severely affects their perceptual quality. In this work, we make one of the first attempts to propose a novel Perceptual Crack Detection (PCD) method for detecting and localizing crack artifacts in rendered meshes. Specifically, motivated by the characteristics of the human visual system (HVS), we adopt contrast and Laplacian measurement modules to characterize crack artifacts and differentiate them from other undesired artifacts. Extensive experiments on large-scale public datasets of 3D textured meshes demonstrate effectiveness and efficiency of the proposed PCD method in correct localization and detection of crack artifacts. Moreover, to quantify the performance of the proposed detection method and validate its effectiveness, we propose a simple yet effective weighting mechanism to incorporate the resulting crack map into classical quality assessment (QA) models, which creates significant performance improvement in predicting the perceptual image quality when tested on public datasets of static 3D textured meshes. A software release of the proposed method is publicly available at: https://github.com/arshafiee/crack-detection-VVM

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9798350361582
ISSN:	2472-7814
Date of First Compliant Deposit:	23 July 2024
Date of Acceptance:	27 March 2024
Last Modified:	08 Nov 2024 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/170878

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