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
https://github.com/cwi-dis/pointpca_suite/.

本文提出了一个计算简化和描述符丰富的点云质量评估（PCQA）指标，名为 PointPCA+，它是 PointPCA 的扩展。PointPCA 提出了一组基于 PCA 分解的感知相关描述符，这些描述符应用于点云的几何和纹理数据以进行完全参考的 PCQA。PointPCA + 仅在几何数据上使用 PCA，同时丰富了现有的几何和纹理描述符，计算效率更高。与 PointPCA 类似，通过学习融合几何和纹理描述符的个别预测来获取总体质量分数，捕捉局部形状和外观特性。在特征融合之前，引入了特征选择模块，从提出的超级集中选择最有效的特征。实验结果表明，PointPCA + 对公开可用数据集获得的主观真实分数具有较高的预测性能。代码可在 https://github.com/cwi-dis/pointpca_suite/ 上获得。