With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shifting, scaling, and rotational invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases.

翻译：随着三维视觉的快速发展，点云已成为日益流行的三维视觉媒体内容。由于不规则结构，点云为压缩、传输、渲染及质量评估等相关研究带来了新挑战。在最新研究中，点云质量评估因在指导实际应用中的重要作用而受到广泛关注，尤其在参考点云不可用的许多场景中。然而，当前基于主流深度神经网络的无参考指标存在明显缺陷。例如，为适配点云的不规则结构，它们需要体素化、投影等预处理步骤，这会引入额外失真；同时，所采用的卷积神经网络等网格核网络难以提取有效的失真相关特征。此外，这些方法极少考虑多种失真模式以及点云质量评估应具备平移、缩放和旋转不变性的核心理念。本文提出了一种新颖的无参考点云质量评估指标——图卷积点云质量评估网络。为提取有效的点云质量评估特征，我们设计了一种新型图卷积核GPAConv，能够自适应地捕获结构与纹理的扰动。随后，我们提出了包含一个主任务（质量回归）和两个辅助任务（失真类型与失真程度预测）的多任务框架。最后，我们引入坐标归一化模块，以稳定GPAConv在平移、缩放和旋转变换下的输出结果。在两个独立数据库上的实验结果表明，GPA-Net的性能优于现有最先进的无参考点云质量评估指标，甚至在某些情况下超越部分全参考指标。