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In recent years, point clouds have become increasingly popular forrepresenting three-dimensional (3D) visual objects and scenes. To efficientlystore and transmit point clouds, compression methods have been developed, butthey often result in a degradation of quality. To reduce color distortion inpoint clouds, we propose a graph-based quality enhancement network (GQE-Net)that uses geometry information as an auxiliary input and graph convolutionblocks to extract local features efficiently. Specifically, we use aparallel-serial graph attention module with a multi-head graph attentionmechanism to focus on important points or features and help them fuse together.Additionally, we design a feature refinement module that takes into account thenormals and geometry distance between points. To work within the limitations ofGPU memory capacity, the distorted point cloud is divided into overlap-allowed3D patches, which are sent to GQE-Net for quality enhancement. To account fordifferences in data distribution among different color components, three modelsare trained for the three color components. Experimental results show that ourmethod achieves state-of-the-art performance. For example, when implementingGQE-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 Crcomponents, respectively. The source code of our method is available athttps://github.com/xjr998/GQE-Net.

GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute