  Full-reference (FR) point cloud quality assessment (PCQA) has achieved
impressive progress in recent years. However, in many cases, obtaining the
reference point cloud is difficult, so the no-reference (NR) methods have
become a research hotspot. Since learning-based FR-PCQA methods should be
driven by data, few researches about NR objective quality metrics are conducted
due to lack of large-scale subjective point cloud dataset. The shortage of
point cloud data restricts the development of NR-PCQA techniques. Besides, the
distinctive property of point cloud format makes infeasible applying blind
image quality assessment (IQA) methods directly to predict the quality scores
of point clouds. In this paper, we establish a large-scale PCQA dataset, which
includes 104 reference point clouds and more than 24,000 distorted point
clouds. In the established dataset, each reference point cloud is augmented
with 34 types of impairments (e.g., Gaussian noise, contrast distortion,
geometry noise, local loss, and compression loss) at 7 different distortion
levels. Besides, inspired by the hierarchical perception system and considering
the intrinsic attribute of point clouds, an end-to-end sparse convolutional
neural network (CNN) is designed to accurately estimate the subjective quality.
We conduct experiments on the new dataset to evaluate the performance of the
proposed network. The results demonstrate that the proposed network has
reliable performance. The dataset presented in this work will be publicly
accessible at http://smt.sjtu.edu.cn.
