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：随着对沉浸式体验兴趣的增加，点云应运而生并被广泛采纳为表示三维媒体的首选。除了从采集到渲染过程中可能影响三维内容的多种失真外，通过传统通信系统高效传输此类体素内容也以牺牲感知质量为代价。为评估此类质量退化的程度，采用质量指标成为不可避免的解决方案。本文提出一种新颖的基于深度学习的无参考质量指标，可直接对整体点云进行操作而无需大量预处理，从而在传输和渲染层面实现实时评估。为此，我们采用以交叉注意力和自注意力层为核心的新型模型设计，在从基础特征提取到深度表示建模的多层级中，保留几何与颜色信息的最佳组合，同时学习最优的局部语义关联集。