Point cloud is one of the most widely used digital representation formats for 3D contents, the visual quality of which may suffer from noise and geometric shift during the production procedure as well as compression and downsampling during the transmission process. To tackle the challenge of point cloud quality assessment (PCQA), many PCQA methods have been proposed to evaluate the visual quality levels of point clouds by assessing the rendered static 2D projections. Although such projection-based PCQA methods achieve competitive performance with the assistance of mature image quality assessment (IQA) methods, they neglect the dynamic quality-aware information, which does not fully match the fact that observers tend to perceive the point clouds through both static and dynamic views. Therefore, in this paper, we treat the point clouds as moving camera videos and explore the way of dealing with PCQA tasks via using video quality assessment (VQA) methods in a no-reference (NR) manner. First, we generate the captured videos by rotating the camera around the point clouds through four circular pathways. Then we extract both spatial and temporal quality-aware features from the selected key frames and the video clips by using trainable 2D-CNN and pre-trained 3D-CNN models respectively. Finally, the visual quality of point clouds is represented by the regressed video quality values. The experimental results reveal that the proposed method is effective for predicting the visual quality levels of the point clouds and even competitive with full-reference (FR) PCQA methods. The ablation studies further verify the rationality of the proposed framework and confirm the contributions made by the quality-aware features extracted from dynamic views.
翻译:虽然这种基于投影的PCQA方法在成熟图像质量评估(IQA)方法的帮助下取得了竞争性的性能,但是它们忽视了动态质量意识信息,而动态质量意识信息并不完全符合以下事实,即观察员往往通过静态和动态观点来看待点云云云。因此,为了应对点云质量评估(PCQA)的挑战,提出了许多PCQA方法,通过评估提供的静态 2D 预测来评估点云的视觉质量水平。尽管这种基于投影的PCQA方法在成熟图像质量评估(IQA)方法的帮助下实现了竞争性的性能,但是它们忽视了动态质量信息,而这与观察员往往通过静态和动态观点来观察点云云云云云云云云云云,因此在本文件中,我们把点云点云视为移动相机质量评估(PCQA),并探索如何通过视频质量评估(VQQA)的方法评估点云的视觉质量水平。我们通过四个循环途径将摄像头生成视频。然后,我们从选定的关键CN框架和视频剪辑中提取了空间和时间质量特征特征特征特征特征特征,我们利用可选的2D级的图像质量分析结果,最后由直观的图像显示的图像质量评估结果。