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.

翻译：3D点云(PC)是展示3D视觉信息的一个大有希望的解决办法和有效手段,除了在浸入应用程序方面的其他挑战之外,对压缩的3D内容进行客观和主观质量评估仍然是尚未解决的问题,也是研究兴趣的领域。然而,研究领域的大部分努力忽视了各点代表之间的当地几何结构。在本文件中,我们通过采用图表神经网络(GNN)学习当地固有的依赖性,为点云质量评估引入了新颖和高效的客观指标,克服了这一限制。为了评估我们的方法的性能,使用了两个众所周知的数据集。结果表明我们的解决办法与最新指标相比是有效和可靠的。