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

Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric