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

• URL: http://arxiv.org/abs/2211.02459v1
• Date: Fri, 4 Nov 2022 13:45:54 GMT
• Title: PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment
• Authors: Marouane Tliba, Aladine Chetouani, Giuseppe Valenzise and Frederic Dufaux
• Abstract summary: 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. This paper introduces a novel and efficient objective metric for Point Clouds Quality Assessment, by learning intrinsic local dependencies using Graph Neural Network (GNN) The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics.
• Score: 11.515951211296361
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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