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3D愿景的快速发展,点云已成为日益流行的3D视觉媒体内容。由于不规则的结构,点云对相关研究提出了新的挑战,如压缩、传输、提供和质量评估。在最新研究中,点云质量评估(PCQA)因其在指导实际应用方面的重要作用而引起广泛关注,特别是在许多没有参考点云的情况下。然而,目前基于普遍存在的深层神经网络的不参照度指标显然有其缺点。例如,为了适应点云的不正常结构,需要预先处理,如引入额外扭曲的氧化和预测,以及应用的电网核心网络,如Convolution Neal网络,未能提取有效的扭曲相关特征。此外,它们很少考虑到各种扭曲模式和理念,即 PCQA应显示变化、缩放和旋转的模块。在本文件中,我们提出了名为“图形革命” PCAQA网络(GPA网络), 甚至要为PCQA带来有效的转换,我们提议了一个更精确的升级, 并展示了最新版本的GIPA(IA) 和多级框架。