使用基于卷积神经网络的一维模型进行点云质量评估
Abdelouahed Laazoufi1, Mohammed El Hassouni2, Hocine Cherifi3
1Research Laboratory in Computer Science and Telecommunications (LRIT), Faculty of Sciences, Mohammed V University in Rabat, Rabat 1014, Morocco.
Journal of imaging
|June 26, 2024
概括
本研究引入了一种新的深度学习方法,用于无参考3D点云质量评估. 该方法有效评估3D模型中的扭曲,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 机器学习 机器学习
背景情况:
- 3D建模的进步影响VR,诊断和架构.
- 由于简化/压缩造成的扭曲会降低3D点云质量.
- 对于扭曲的3D数据,客观的质量评估方法至关重要.
研究的目的:
- 为3D点云质量评估开发一种新的无参考 (NR) 深度学习方法.
- 为满足对扭曲的3D点云进行可靠和有效的客观质量评估的需求.
- 提高用于各种应用的3D模型质量评估的准确性.
主要方法:
- 从扭曲的3D点云中提取几何和感知属性.
- 属性的表示作为1D向量用于特征提取.
- 使用从二维CNN中改编的1D卷积神经网络 (1D CNN) 进行转移学习的应用.
- 使用完全连接层的回归进行质量评分预测.
主要成果:
- 拟议的NR方法在3D点云质量评估中表现出卓越的性能.
- 该方法显示了与多个数据集的平均意见分数的增强相关性.
- 在SJTU_PCQA,WPC和ICIP2020数据库上进行评估,取得了最先进的结果.
结论:
- 基于深度学习的NR方法为3D点云质量评估提供了有效的解决方案.
- 该方法提供了一种可靠和有效的方法来评估3D模型中的扭曲.
- 这项工作有助于推进对3D数据的客观质量评估.
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