  Following the advent of immersive technologies and the increasing interest in
representing interactive geometrical format, 3D Point Clouds (PC) have emerged
as a promising solution and effective means to display 3D visual information.
In addition to other challenges in immersive applications, objective and
subjective quality assessments of compressed 3D content remain open problems
and an area of research interest. Yet most of the efforts in the research area
ignore the local geometrical structures between points representation. In this
paper, we overcome this limitation by introducing a novel and efficient
objective metric for Point Clouds Quality Assessment, by learning local
intrinsic dependencies using Graph Neural Network (GNN). To evaluate the
performance of our method, two well-known datasets have been used. The results
demonstrate the effectiveness and reliability of our solution compared to
state-of-the-art metrics.
