PointPCA+: Extending PointPCA Objective Quality Assessment Metric
Xuemei Zhou, Evangelos Alexiou, Irene Viola, Pablo César
Abstract
A computationally-simplified and descriptor-richer Point Cloud Quality Assessment (PCQA) metric, namely Point-PCA+, 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 https://github.com/cwi-dis/pointpca_suite/.