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Visual Quality of 3D Meshes With Diffuse Colors in Virtual Reality: Subjective and Objective Evaluation

机译:虚拟现实中漫射颜色的3D网格的视觉质量:主观和客观评估

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Surface meshes associated with diffuse texture or color attributes are becoming popular multimedia contents. They provide a high degree of realism and allow six degrees of freedom (6DoF) interactions in immersive virtual reality environments. Just like other types of multimedia, 3D meshes are subject to a wide range of processing, e.g., simplification and compression, which result in a loss of quality of the final rendered scene. Thus, both subjective studies and objective metrics are needed to understand and predict this visual loss. In this work, we introduce a large dataset of 480 animated meshes with diffuse color information, and associated with perceived quality judgments. The stimuli were generated from 5 source models subjected to geometry and color distortions. Each stimulus was associated with 6 hypothetical rendering trajectories (HRTs): combinations of 3 viewpoints and 2 animations. A total of 11520 quality judgments (24 per stimulus) were acquired in a subjective experiment conducted in virtual reality. The results allowed us to explore the influence of source models, animations and viewpoints on both the quality scores and their confidence intervals. Based on these findings, we propose the first metric for quality assessment of 3D meshes with diffuse colors, which works entirely on the mesh domain. This metric incorporates perceptually-relevant curvature-based and color-based features. We evaluate its performance, as well as a number of Image Quality Metrics (IQMs), on two datasets: ours and a dataset of distorted textured meshes. Our metric demonstrates good results and a better stability than IQMs. Finally, we investigated how the knowledge of the viewpoint (i.e., the visible parts of the 3D model) may improve the results of objective metrics.
机译:与漫射纹理或颜色属性相关联的表面网格正在成为流行的多媒体内容。它们提供了高度的现实主义,并允许六个自由(6dof)在沉浸式虚拟现实环境中进行相互作用。就像其他类型的多媒体一样,3D网格受到广泛的处理,例如简化和压缩,这导致最终呈现场景的质量损失。因此,需要理解和预测这种视觉损失所需的主观研究和客观度量。在这项工作中,我们引入了一个带有漫反射颜色信息的480个动画网格的大型数据集,并与感知质量判断相关联。从经过几何形状和颜色扭曲的5个源模型产生刺激。每个刺激与6个假想渲染轨迹(HRT)相关联:3个观点和2个动画的组合。在虚拟现实的主观实验中获得了总共11520个质量判断(每次刺激24)。结果允许我们探讨源模型,动画和观点对质量分数及其置信区间的影响。基于这些调查结果,我们提出了第一批度量标准对具有漫反射颜色的3D网格的质量评估,这完全在网状域上工作。该度量包含感知相关的基于曲率和基于颜色的特征。我们在两个数据集中评估其性能,以及许多图像质量指标(IQMS):我们和扭曲纹理网格的数据集。我们的指标展示了良好的结果和比IQM更好的稳定性。最后,我们研究了观点知识(即3D模型的可见部分)的知识可以改善目标度量的结果。

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