SJTU-TMQA: A quality assessment database for static mesh with texture map
This addresses a gap in 3D graphics for applications like animation and gaming, but it is incremental as it primarily provides a new benchmark dataset.
The authors tackled the lack of quality assessment for textured meshes by creating SJTU-TMQA, a large-scale database with 21 reference meshes and 945 distorted samples, and found that existing objective metrics achieve only around 0.6 correlation with human scores.
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