PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment

TL;DR

This paper introduces PCQA-GRAPHPOINT, an efficient graph neural network-based metric for assessing the quality of 3D point clouds, addressing the lack of local geometric structure consideration in existing methods.

Contribution

The paper presents a novel GNN-based objective metric that captures local intrinsic dependencies in point clouds for improved quality assessment.

Findings

Outperforms state-of-the-art metrics on benchmark datasets

Effectively captures local geometric structures

Demonstrates high reliability and efficiency

Abstract

Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, objective and subjective quality assessments of compressed 3D content remain open problems and an area of research interest. Yet most of the efforts in the research area ignore the local geometrical structures between points representation. In this paper, we overcome this limitation by introducing a novel and efficient objective metric for Point Clouds Quality Assessment, by learning local intrinsic dependencies using Graph Neural Network (GNN). To evaluate the performance of our method, two well-known datasets have been used. The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics.