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.

翻译：过去十年间，三维图形为模拟真实世界而变得高度精细，导致其尺寸与复杂度急剧膨胀。特定应用场景与设备限制要求对其进行简化及/或有损压缩，这可能导致视觉质量下降。因此，为确保最佳体验质量（QoE），准确评估视觉质量以精确驱动压缩过程，并在视觉质量与数据规模之间找到合理折中方案至关重要。本研究聚焦于带纹理三维网格的主观与客观质量评估。我们首先构建了一个大规模数据集，包含55个源模型，这些模型在几何、颜色和语义复杂度方面进行了量化表征，并受到五种基于压缩的失真组合影响，这些失真作用于网格的几何、纹理映射与纹理图像。该数据集包含超过34.3万个失真刺激样本。我们提出了一种方法，从中选取了3000个具有挑战性的刺激子集，并通过大规模众包主观实验，收集了来自4500余名参与者的148929个质量评判结果。基于我们的主观标注数据集，我们提出了一种面向三维图形的基于学习的质量评价指标。该指标在我们构建的纹理网格数据集以及带有顶点颜色的失真网格数据集上均展现出最先进性能。最后，我们展示了利用该指标与数据集探究失真交互作用与内容特征对压缩纹理网格感知质量影响的典型应用案例。