  The real-world applications of 3D point clouds have been growing rapidly in
recent years, but not much effective work has been dedicated to perceptual
quality assessment of colored 3D point clouds. In this work, we first build a
large 3D point cloud database for subjective and objective quality assessment
of point clouds. We construct 20 high quality, realistic, and omni-directional
point clouds of diverse contents. We then apply downsampling, Gaussian noise,
and three types of compression algorithms to create 740 distorted point clouds.
We carry out a subjective experiment to evaluate the quality of distorted point
clouds. Our statistical analysis finds that existing objective point cloud
quality assessment (PCQA) models only achieve limited success in predicting
subjective quality ratings. We propose a novel objective PCQA model based on
the principle of information content weighted structural similarity. Our
experimental results show that the proposed model well correlates with
subjective opinions and significantly outperforms the existing PCQA models. The
database has been made publicly available to facilitate reproducible research
at https://github.com/qdushl/Waterloo-Point-Cloud-Database.
