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GPA-Net:No-Reference Point Cloud Quality Assessment With Multi-Task Graph Convolutional Network.

Ziyu Shan, Qi Yang, Rui Ye

IEEE Transactions on Visualization and Computer Graphics

|June 28, 2023

Summary

This summary is machine-generated.

A new Graph convolutional Point Cloud Quality Assessment network (GPA-Net) effectively assesses 3D point cloud quality without a reference. GPA-Net overcomes limitations of existing methods by using graph convolutions and a multi-task framework for accurate, invariant quality prediction.

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Area of Science:

3D Computer Vision
Multimedia Signal Processing
Machine Learning

Background:

Point clouds are increasingly popular 3D visual data, posing challenges for quality assessment due to their irregular structure.
Current no-reference point cloud quality assessment (PCQA) metrics often require preprocessing that introduces distortions and fail to capture essential features.
Existing deep learning methods struggle with the unique characteristics of point clouds, including various distortion patterns and the need for transformation invariance.

Purpose of the Study:

To propose a novel no-reference point cloud quality assessment (PCQA) metric, GPA-Net, that overcomes the limitations of existing methods.
To develop a PCQA metric that is invariant to shift, scaling, and rotation transformations.
To provide a robust method for quality assessment when a reference point cloud is unavailable.

Main Methods:

Introduced a novel Graph convolutional PCQA network (GPA-Net) utilizing a new graph convolution kernel (GPAConv) to capture structural and textural perturbations.
Implemented a multi-task learning framework for quality regression, distortion type, and degree prediction.
Developed a coordinate normalization module to ensure invariance to shift, scale, and rotation transformations.

Main Results:

GPA-Net demonstrated superior performance compared to state-of-the-art no-reference PCQA metrics on independent databases.
In certain scenarios, GPA-Net even outperformed some full-reference quality assessment metrics.
The proposed GPAConv effectively extracts distortion-related features from irregular point cloud data.

Conclusions:

GPA-Net offers a significant advancement in no-reference PCQA, providing accurate and robust quality assessment for 3D point clouds.
The method's invariance properties and effective feature extraction address key challenges in current PCQA research.
GPA-Net shows potential for practical applications where reference point clouds are not available.