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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Jinrui Xing
  • Hui Yuan
  • Raouf Hamzaoui
  • Hao Liu
  • Junhui Hou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)6303-6317
Journal / PublicationIEEE Transactions on Image Processing
Volume32
Online published9 Nov 2023
Publication statusPublished - 2023

Abstract

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 Bjφntegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3% and 14.5% BD-rate savings were achieved on dense point clouds for the Y, Cb, and Cr components, respectively. he source code of our method is available at https://github.com/xjr998/GQE-Net. © 2023 IEEE.

Research Area(s)

  • point cloud, quality enhancement, graph neural network, G-PCC

Citation Format(s)

GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute. / Xing, Jinrui; Yuan, Hui; Hamzaoui, Raouf et al.
In: IEEE Transactions on Image Processing, Vol. 32, 2023, p. 6303-6317.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review