Yu Haotian, Chen Xiaoyu, Zhang Zhao, Zuo Chao, Zhang Yi, Zheng Dongliang, Han Jing
Opt Express. 2020 Mar 30;28(7):9405-9418. doi: 10.1364/OE.387215.
Fringe projection profilometry (FPP) has become increasingly important in dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of the measured object before shape profiling. However, traditional phase retrieval techniques often require a large number of fringes, which may generate motion-induced error for dynamic objects. In this paper, a novel phase retrieval technique based on deep learning is proposed, which uses an end-to-end deep convolution neural network to transform a single or two fringes into the phase retrieval required fringes. When the object's surface is located in a restricted depth, the presented network only requires a single fringe as the input, which otherwise requires two fringes in an unrestricted depth. The proposed phase retrieval technique is first theoretically analyzed, and then numerically and experimentally verified on its applicability for dynamic 3-D measurement.
条纹投影轮廓术（FPP）在动态三维形状测量中变得越来越重要。在FPP中，在进行形状轮廓分析之前有必要获取被测物体的相位。然而，传统的相位检索技术通常需要大量条纹，这可能会给动态物体产生运动引起的误差。本文提出了一种基于深度学习的新型相位检索技术，该技术使用端到端深度卷积神经网络将单条纹或双条纹转换为相位检索所需的条纹。当物体表面位于受限深度时，所提出的网络仅需要单条纹作为输入，否则在非受限深度需要双条纹。首先对所提出的相位检索技术进行了理论分析，然后对其在动态三维测量中的适用性进行了数值和实验验证。
