PointPCA+: Extending PointPCA objective quality assessment metric
Xuemei Zhou Evangelos Alexiou Irene Viola Pablo Cesar
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/}.