  Full-reference (FR) point cloud quality assessment (PCQA) has achieved
impressive progress in recent years. However, in many cases, obtaining the
reference point clouds is difficult, so no-reference (NR) metrics have become a
research hotspot. Few researches about NR-PCQA are carried out due to the lack
of a large-scale PCQA dataset. In this paper, we first build a large-scale PCQA
dataset named LS-PCQA, which includes 104 reference point clouds and more than
22,000 distorted samples. In the dataset, each reference point cloud is
augmented with 31 types of impairments (e.g., Gaussian noise, contrast
distortion, local missing, and compression loss) at 7 distortion levels.
Besides, each distorted point cloud is assigned with a pseudo quality score as
its substitute of Mean Opinion Score (MOS). Inspired by the hierarchical
perception system and considering the intrinsic attributes of point clouds, we
propose a NR metric ResSCNN based on sparse convolutional neural network (CNN)
to accurately estimate the subjective quality of point clouds. We conduct
several experiments to evaluate the performance of the proposed NR metric. The
results demonstrate that ResSCNN exhibits the state-of-the-art (SOTA)
performance among all the existing NR-PCQA metrics and even outperforms some FR
metrics. The dataset presented in this work will be made publicly accessible at
http://smt.sjtu.edu.cn. The source code for the proposed ResSCNN can be found
at https://github.com/lyp22/ResSCNN.
