PointPCA+: Extending PointPCA objective quality assessment metric

TL;DR

PointPCA+ is a simplified, descriptor-rich point cloud quality assessment metric that focuses on geometry data, improves efficiency, and achieves high predictive accuracy through feature selection and learning-based fusion.

Contribution

It extends PointPCA by focusing PCA on geometry data, enriching descriptors, and introducing feature selection for improved efficiency and accuracy.

Findings

High predictive performance on public datasets

Efficient computation of geometry and texture descriptors

Effective feature selection improves quality prediction

Abstract

A computationally-simplified and descriptor-richer Point Cloud Quality Assessment (PCQA) metric, namely PointPCA+, is proposed in this paper, which is an extension of PointPCA. PointPCA proposed a set of perceptually-relevant descriptors based on PCA decomposition that were applied to both the geometry and texture data of point clouds for full reference PCQA. PointPCA+ employs PCA only on the geometry data while enriching existing geometry and texture descriptors, that are computed more efficiently. Similarly to PointPCA, a total quality score is obtained through a learning-based fusion of individual predictions from geometry and texture descriptors that capture local shape and appearance properties, respectively. Before feature fusion, a feature selection module is introduced to choose the most effective features from a proposed super-set. Experimental results show that PointPCA+ achieves high predictive performance against subjective ground truth scores obtained from publicly available datasets. The code is available at \url{https://github.com/cwi-dis/pointpca_suite/}.