  In recent years, point clouds have become increasingly popular for
representing three-dimensional (3D) visual objects and scenes. To efficiently
store and transmit point clouds, compression methods have been developed, but
they often result in a degradation of quality. To reduce color distortion in
point clouds, we propose a graph-based quality enhancement network (GQE-Net)
that uses geometry information as an auxiliary input and graph convolution
blocks to extract local features efficiently. Specifically, we use a
parallel-serial graph attention module with a multi-head graph attention
mechanism to focus on important points or features and help them fuse together.
Additionally, we design a feature refinement module that takes into account the
normals and geometry distance between points. To work within the limitations of
GPU memory capacity, the distorted point cloud is divided into overlap-allowed
3D patches, which are sent to GQE-Net for quality enhancement. To account for
differences in data distribution among different color components, three models
are trained for the three color components. Experimental results show that our
method achieves state-of-the-art performance. For example, when implementing
GQE-Net on a recent test model of the geometry-based point cloud compression
(G-PCC) standard, 0.43 dB, 0.25 dB, and 0.36 dB Bjontegaard delta
(BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3%, and 14.5%
BD-rate savings can be achieved on dense point clouds for the Y, Cb, and Cr
components, respectively. The source code of our method is available at
https://github.com/xjr998/GQE-Net.
