  With the rapid development of 3D vision, point cloud has become an
increasingly popular 3D visual media content. Due to the irregular structure,
point cloud has posed novel challenges to the related research, such as
compression, transmission, rendering and quality assessment. In these latest
researches, point cloud quality assessment (PCQA) has attracted wide attention
due to its significant role in guiding practical applications, especially in
many cases where the reference point cloud is unavailable. However, current
no-reference metrics which based on prevalent deep neural network have apparent
disadvantages. For example, to adapt to the irregular structure of point cloud,
they require preprocessing such as voxelization and projection that introduce
extra distortions, and the applied grid-kernel networks, such as Convolutional
Neural Networks, fail to extract effective distortion-related features.
Besides, they rarely consider the various distortion patterns and the
philosophy that PCQA should exhibit shifting, scaling, and rotational
invariance. In this paper, we propose a novel no-reference PCQA metric named
the Graph convolutional PCQA network (GPA-Net). To extract effective features
for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which
attentively captures the perturbation of structure and texture. Then, we
propose the multi-task framework consisting of one main task (quality
regression) and two auxiliary tasks (distortion type and degree predictions).
Finally, we propose a coordinate normalization module to stabilize the results
of GPAConv under shift, scale and rotation transformations. Experimental
results on two independent databases show that GPA-Net achieves the best
performance compared to the state-of-the-art no-reference PCQA metrics, even
better than some full-reference metrics in some cases.
