论文标题
PCQA-GRAPHPORT：效果深度图指标用于点云质量评估
PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment
论文作者
论文摘要
在沉浸式技术的出现以及对代表交互式几何格式的日益兴趣之后，出现了3D点云（PC）作为有希望的解决方案和有效的方法来显示3D视觉信息。除了沉浸式应用中的其他挑战外，压缩3D内容的客观和主观质量评估仍然是开放的问题和研究兴趣领域。然而，研究领域的大多数努力都忽略了点表示之间的局部几何结构。在本文中，我们通过使用图形神经网络（GNN）来学习局部固有依赖关系来克服这种局限性，以进行云质量评估的新颖而有效的客观度量。为了评估我们方法的性能，已经使用了两个著名的数据集。与最先进的指标相比，结果证明了解决方案的有效性和可靠性。
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.
