With the increased interest in immersive experiences, point cloud came to birth and was widely adopted as the first choice to represent 3D media. Besides several distortions that could affect the 3D content spanning from acquisition to rendering, efficient transmission of such volumetric content over traditional communication systems stands at the expense of the delivered perceptual quality. To estimate the magnitude of such degradation, employing quality metrics became an inevitable solution. In this work, we propose a novel deep-based no-reference quality metric that operates directly on the whole point cloud without requiring extensive pre-processing, enabling real-time evaluation over both transmission and rendering levels. To do so, we use a novel model design consisting primarily of cross and self-attention layers, in order to learn the best set of local semantic affinities while keeping the best combination of geometry and color information in multiple levels from basic features extraction to deep representation modeling.
翻译:随着对沉浸式经验的兴趣增加,点云诞生,被广泛接受为代表3D媒体的第一种选择。除了可能影响从获取到传播的三维内容的若干扭曲现象外,在传统通信系统上有效传输这种量体内容也牺牲了所提供的感官质量。为了估计这种退化的程度,采用质量指标就成为一种不可避免的解决办法。在这项工作中,我们提议了一个新的深基不参考质量指标,直接在整个点云上运行,而不需要广泛的预处理,从而能够对传输和传输水平进行实时评价。为了做到这一点,我们使用一种新颖的模式设计,主要包括交叉和自我注意层,以便学习最佳的当地语义近似关系,同时把从基本特征提取到深代表模型的多层次的几何和颜色信息最佳组合。</s>