  Over the past decade, 3D graphics have become highly detailed to mimic the
real world, exploding their size and complexity. Certain applications and
device constraints necessitate their simplification and/or lossy compression,
which can degrade their visual quality. Thus, to ensure the best Quality of
Experience (QoE), it is important to evaluate the visual quality to accurately
drive the compression and find the right compromise between visual quality and
data size. In this work, we focus on subjective and objective quality
assessment of textured 3D meshes. We first establish a large-scale dataset,
which includes 55 source models quantitatively characterized in terms of
geometric, color, and semantic complexity, and corrupted by combinations of 5
types of compression-based distortions applied on the geometry, texture mapping
and texture image of the meshes. This dataset contains over 343k distorted
stimuli. We propose an approach to select a challenging subset of 3000 stimuli
for which we collected 148929 quality judgments from over 4500 participants in
a large-scale crowdsourced subjective experiment. Leveraging our subject-rated
dataset, a learning-based quality metric for 3D graphics was proposed. Our
metric demonstrates state-of-the-art results on our dataset of textured meshes
and on a dataset of distorted meshes with vertex colors. Finally, we present an
application of our metric and dataset to explore the influence of distortion
interactions and content characteristics on the perceived quality of compressed
textured meshes.
