Point clouds denote a prominent solution for the representation of 3D photo-realistic content in immersive applications. Similarly to other imaging modalities, quality predictions for point cloud contents are vital for a wide range of applications, enabling trade-off optimizations between data quality and data size in every processing step from acquisition to rendering. In this work, we focus on use cases that consider human end-users consuming point cloud contents and, hence, we concentrate on visual quality metrics. In particular, we propose a set of perceptually relevant descriptors based on Principal Component Analysis (PCA) decomposition, which is applied to both geometry and texture data for full-reference point cloud quality assessment. Statistical features are derived from these descriptors to characterize local shape and appearance properties for both a reference and a distorted point cloud. The extracted statistical features are subsequently compared to provide corresponding predictions of visual quality for the distorted point cloud. As part of our method, a learning-based approach is proposed to fuse these individual predictors to a unified perceptual score. We validate the accuracy of the individual predictors, as well as the unified quality scores obtained after regression against subjectively annotated datasets, showing that our metric outperforms state-of-the-art solutions. Insights regarding design decisions are provided through exploratory studies, evaluating the performance of our metric under different parameter configurations, attribute domains, color spaces, and regression models. A software implementation of the proposed metric is made available at the following link: https://github.com/cwi-dis/pointpca_suite.

翻译：点云是沉浸式应用中表示三维逼真内容的一种主要解决方案。与其他成像模态类似，点云内容的质量预测对广泛应用至关重要，可在从采集到渲染的每个处理步骤中实现数据质量与数据大小之间的权衡优化。本研究聚焦于以人类终端用户消费点云内容为目标的用例，因此我们主要关注视觉质量指标。具体而言，我们提出一组基于主成分分析（PCA）分解的感知相关描述符，将其应用于几何与纹理数据以实现全参考点云质量评估。从这些描述符中提取统计特征，用于表征参考点云与失真点云的局部形状与外观属性。随后对这些提取的统计特征进行比较，以提供对失真点云视觉质量的相应预测。作为方法的一部分，我们提出一种基于学习的方法将这些单个预测因子融合为统一的感知分数。我们验证了单个预测因子的准确性，以及经回归后与主观标注数据集对比得到的统一质量分数，结果表明我们的指标优于现有最优方案。通过探索性研究提供关于设计选择的见解，评估了我们的指标在不同参数配置、属性域、色彩空间及回归模型下的性能。所提指标的软件实现可从以下链接获取：https://github.com/cwi-dis/pointpca_suite。