.png){kind=icon}
返回
Micro deep learning profilometry for high-speed 3D surface imaging
.png){kind=icon}
PRE
AI
DOI：10.1016/j.optlaseng.2019.04.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
How to obtain object information as rich as possible, with the highest possible speed and accuracy from recorded optical signals, has been a crucial issue to the pursuit of powerful imaging technologies. Nowadays, the speed of ultra-fast photography can exceed one quadrillion. However, it can record only two-dimensional images which lack the depth information, greatly limiting our ability to perceive and to understand the complex real-world objects. Inspired by recent successes of deep learning methods in computer vision, we present a novel high-speed three-dimensional (3D) surface imaging approach named micro deep learning profilometry (mu DLP) using the structured light illumination. With a properly trained deep neural network, the phase information is predicted from a single fringe image and then can be converted into the 3D shape. Our experiments demonstrate that mu DLP can faithfully retrieve the geometry of dynamic objects at 20,000 frames per second. Moreover, comparative results show that mu DLP has superior performance in terms of the phase accuracy, reconstruction efficiency, and the ease of implementation over widely used Fourier-transform-based fast 3D imaging techniques, verifying that mu DLP is a powerful high-speed 3D surface imaging approach.
Keyword:
Deep learning
3D surface imaging
Structured light
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
7.3K
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
Review of single-shot 3D shape measurement by phase calculation-based fringe projection techniques
PREAI
分享收藏分享收藏分享收藏
查看更多内容