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Communication Dans Un Congrès Année : 2023

Deep Learning Multi-View Fusion for Blind 3D Point Cloud Quality Assessment

Résumé

In recent years, 3D point clouds (3DPC) have experienced rapid growth in various fields of computer vision, leading to an increased demand for efficient approaches to automatically assess the quality of 3D point clouds. In this work, we propose a deep learning-based method for No-Reference (Blind) Point Cloud Quality Assessment (NR-PCQA) that aims to automatically predict the perceived visual quality of the 3DPC without relying on a reference content. We evaluate the performance of our model on two benchmark databases: SJTU and WPC. Experimental results show that our model achieves good performance compared to state-of-the-art methods.
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Dates et versions

hal-04264170 , version 1 (30-10-2023)

Identifiants

  • HAL Id : hal-04264170 , version 1

Citer

Salima Bourbia, Ayoub Karine, Aladine Chetouani, Mohammed El Hassouni, Maher Jridi. Deep Learning Multi-View Fusion for Blind 3D Point Cloud Quality Assessment. Affinity Workshop NAML (North Africans in Machine Learning Workshop) of NeurIPS 2023, Dec 2023, New Orleans, United States. ⟨hal-04264170⟩
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