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

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+…
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Taxonomy
Topics3D Shape Modeling and Analysis · 3D Surveying and Cultural Heritage · Remote Sensing and LiDAR Applications
MethodsSparse Evolutionary Training · Principal Components Analysis · Feature Selection
