基于简化光场图像采集方法和完全连接的深度神经网络的全面高质量三维显示系统
Munkh-Uchral Erdenebat1, Tuvshinjargal Amgalan1, Anar Khuderchuluun1
1School of Information and Communication Engineering, Chungbuk National University, Chungbuk 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种新的高质量3D可视化系统,使用简化的光场图像采集方法和定制深度神经网络. 该系统有效地捕获和重建来自现实世界对象的详细3D信息,以实现整体成像显示.
科学领域:
- 计算机视觉 计算机视觉
- 3D显示技术 3D显示技术
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 传统的光场采集可以是复杂和资源密集的.
- 实现高分辨率的3D可视化通常需要复杂的多摄像头设置.
- 一般环境对准确的3D数据捕获提出了挑战.
研究的目的:
- 使用简化光场图像采集方法开发高质量的3D显示系统.
- 在一般环境中以高质量重建现实世界对象的光场图像.
- 在整体成像显示器上实现高分辨率的3D可视化.
主要方法:
- 采用简化的光场图像采集方法来捕获3D信息.
- 一个定制训练的,完全连接的深度神经网络被用于视角重建.
- 有哈希编码的即时神经图形原始人被训练来生成所需的视角.
- 为可视化,对元素图像数组进行了像素重新排列.
主要成果:
- 拟议的系统成功获取和重建了高质量的光场图像.
- 深度神经网络以始终高质量的方式产生了所需的视角.
- 该系统在一个完整的成像显示器上实现了高分辨率的3D可视化.
- 使用自定义神经图形原始模型,处理时间显著减少.
结论:
- 开发的系统提供了一种简单而有效的方法,用于从真实物体中获取光场图像.
- 在整体成像显示器上实现高质量的3D可视化,可以通过拟议的方法实现.
- 该系统展示了高级3D显示应用的潜力.
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