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/.

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Ignite the Immersive Media Sector by Enabling New Narrative Visions
30th IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Zhou, X., Alexiou, E., Viola, I., & César Garcia, P. S. (2023). PointPCA+: Extending PointPCA objective quality assessment metric. In Proceedings of IEEE International Conference on Image Processing (pp. 3642–3646). doi:10.1109/ICIPC59416.2023.10328338