  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.
