  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
