SJTU-TMQA: A quality assessment database for static mesh with texture map

TL;DR

This paper introduces SJTU-TMQA, a large-scale database for assessing the quality of textured 3D meshes, providing a foundation for developing better quality metrics and improving related applications.

Contribution

The creation of the SJTU-TMQA database with subjective scores and evaluation of existing metrics for textured mesh quality assessment.

Findings

Highest correlation of 0.6 for current metrics

Diverse content validates dataset heterogeneity

Impact of different distortions on perception demonstrated

Abstract

In recent years, static meshes with texture maps have become one of the most prevalent digital representations of 3D shapes in various applications, such as animation, gaming, medical imaging, and cultural heritage applications. However, little research has been done on the quality assessment of textured meshes, which hinders the development of quality-oriented applications, such as mesh compression and enhancement. In this paper, we create a large-scale textured mesh quality assessment database, namely SJTU-TMQA, which includes 21 reference meshes and 945 distorted samples. The meshes are rendered into processed video sequences and then conduct subjective experiments to obtain mean opinion scores (MOS). The diversity of content and accuracy of MOS has been shown to validate its heterogeneity and reliability. The impact of various types of distortion on human perception is demonstrated. 13 state-of-the-art objective metrics are evaluated on SJTU-TMQA. The results report the highest correlation of around 0.6, indicating the need for more effective objective metrics. The SJTU-TMQA is available at https://ccccby.github.io