The evolution of compression and enhancement algorithms necessitates an accurate quality assessment for point clouds. Previous works consistently regard point cloud quality assessment (PCQA) as a MOS regression problem and devise a deterministic mapping, ignoring the stochasticity in generating MOS from subjective tests. Besides, the viewpoint switching of 3D point clouds in subjective tests reinforces the judging stochasticity of different subjects compared with traditional images. This work presents the first probabilistic architecture for no-reference PCQA, motivated by the labeling process of existing datasets. The proposed method can model the quality judging stochasticity of subjects through a tailored conditional variational autoencoder (CVAE) and produces multiple intermediate quality ratings. These intermediate ratings simulate the judgments from different subjects and are then integrated into an accurate quality prediction, mimicking the generation process of a ground truth MOS. Specifically, our method incorporates a Prior Module, a Posterior Module, and a Quality Rating Generator, where the former two modules are introduced to model the judging stochasticity in subjective tests, while the latter is developed to generate diverse quality ratings. Extensive experiments indicate that our approach outperforms previous cutting-edge methods by a large margin and exhibits gratifying cross-dataset robustness.

翻译：压缩与增强算法的演进对点云质量评估的准确性提出了更高要求。现有工作将点云质量评估（PCQA）视为平均主观得分（MOS）回归问题，采用确定性映射方法，忽略了主观测试中生成MOS的随机性。此外，与传统图像相比，三维点云在主观测试中的视点切换进一步增强了不同受试者判断的随机性。本文受现有数据集标注过程启发，首次提出面向无参考PCQA的概率架构。所提方法通过定制条件变分自编码器（CVAE）对受试者的质量判断随机性进行建模，并生成多个中间质量评分。这些中间评分模拟多个受试者的判断结果，经整合后形成精确的质量预测，模拟真实MOS的生成过程。具体而言，该方法包含先验模块、后验模块与质量评分生成器，前两个模块用于建模主观测试中的判断随机性，后一模块用于生成多样化质量评分。大量实验表明，本方法以显著优势超越现有前沿方法，并展现出优异的跨数据集鲁棒性。