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基于深度学习的联合几何学和特征对大规模彩色点云进行提取样本
概括
我们介绍了一种深度学习方法,用于联合几何和属性提升采样 (JGAU),以创建更密集的彩色点云. 这种方法显著提高了上样品的质量,优于现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 机器学习 机器学习
背景情况:
- 彩色点云对于现实的3D应用至关重要.
- 现有的方法难以产生大规模的,密集的彩色点云.
研究的目的:
- 开发一种基于深度学习的方法,用于关节几何学和颜色点云的属性取样.
- 建立一个大规模的数据集,用于彩色点云提取样本研究.
主要方法:
- 提出了一个联合几何和属性提取样本 (JGAU) 框架,具有单独的几何和属性网络.
- 引入了几何距离加权属性互插 (GDWAI) 和基于深度学习的属性互插 (DLAI) 用于粗略的属性提升采样.
- 开发了一个属性增强模块,使用几何和属性模式来完善属性.
主要成果:
- 获得的PSNR值为33.90 dB (4x),32.10 dB (8x),31.10 dB (12x) 和30.39 dB (16x).
- 与最先进的方法相比,证明了显著的PSNR收益,平均为2.32dB,2.47dB,2.28dB和2.11dB,在各自的升级采样率下.
- 发布了SYSU-PCUD数据集,其中包含121个大规模的彩色点云.
结论:
- 该JGAU方法有效地提高了彩色点云的密度和质量.
- 拟议的方法比现有的最先进的升级采样技术提供了实质性的改进.
- SYSU-PCUD数据集有助于进一步研究彩色点云提升采样.
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