  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.
