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Communication Dans Un Congrès Année : 2023

PCQA-Graphpoint: Efficient Deep-Based Graph Metric for Point Cloud Quality Assessment

Résumé

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 overcame previous limitation by introducing a novel and efficient objective metric for Point Clouds Quality Assessment, throughout 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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Dates et versions

hal-04026860 , version 1 (26-04-2023)

Identifiants

Citer

Marouane Tliba, Aladine Chetouani, Giuseppe Valenzise, Frédéric Dufaux. PCQA-Graphpoint: Efficient Deep-Based Graph Metric for Point Cloud Quality Assessment. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Jun 2023, Rhodes Island, Greece. ⟨10.1109/icassp49357.2023.10096610⟩. ⟨hal-04026860⟩
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