期刊
OPTICS COMMUNICATIONS
卷 489, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.optcom.2021.126887
关键词
Fringe Projection Profilometry; Absolute phase; Deep learning
类别
资金
- Natural Science Foundation of Jiangsu Province of China [BK20181269]
- Shenzhen Science and Technology Innovation Committee (STIC) , China [JCYJ201803061744 55080]
- Special Project on Basic Research of Frontier Leading Technology of Jiangsu Province of China [BK20192004C]
A multi-purpose neural network combined with code-based patterns is proposed to efficiently recover absolute phase and decrease pattern quantity, showing potential for high accuracy in complex texture objects.
Fringe Projection Profilometry is one of the important techniques in three-dimensional vision. However, traditional Fringe Projection Profilometry employing a lot of patterns has significant challenges for efficiently recovering the absolute phase. In this paper, a multi-purpose neural network combining with code-based patterns is proposed to recover the absolute phase firstly, which can greatly decrease the number of patterns with high accuracy. Different from the traditional approach of trigonometric calculation, the well-trained multi-purpose network can learn the principle of extracting absolute phase from few patterns. Experiments demonstrate that the proposed method can acquire high accuracy for complex texture objects, indicating potential application in precision engineering with high speed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据