PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors

TL;DR

PointPCA introduces PCA-based descriptors for assessing the visual quality of point clouds, combining geometry and texture analysis with a learning-based fusion to outperform existing metrics.

Contribution

The paper presents a novel PCA-based descriptor set for full-reference point cloud quality assessment, integrating statistical features with a learning approach for improved accuracy.

Findings

Outperforms state-of-the-art quality metrics on subjective datasets

Effective in capturing local shape and appearance distortions

Flexible design validated across different parameters and color spaces

Abstract

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.