Wavelet based deep learning for depth estimation from single fringe pattern of fringe projection profilometry

作     者：ZHU Xinjun HAN Zhiqiang SONG Limei WANG Hongyi WU Zhichao 

作者机构：School of Artificial IntelligenceTiangong UniversityTianjin 300387China 

出 版 物：《Optoelectronics Letters》 (光电子快报（英文版）)

年 卷 期：2022年第18卷第11期

页      面：699-704页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (No.2019KJ021) 

主　　题：measurement network estimation 

摘      要：Depth estimation from single fringe pattern is a fundamental task in the field of fringe projection three-dimensional(3D) measurement. Deep learning based on a convolutional neural network(CNN) has attracted more and more attention in fringe projection profilometry(FPP). However, most of the studies focus on complex network architecture to improve the accuracy of depth estimation with deeper and wider network architecture, which takes greater computational and lower speed. In this letter, we propose a simple method to combine wavelet transform and deep learning method for depth estimation from the single fringe pattern. Specially, the fringe pattern is decomposed into low-frequency and high-frequency details by the two-dimensional(2D) wavelet transform, which are used in the CNN network. Experiment results demonstrate that the wavelet-based deep learning method can reduce the computational complexity of the model by 4 times and improve the accuracy of depth estimation. The proposed wavelet-based deep learning models(UNet-Wavelet and hNet-Wavelet) are efficient for depth estimation of single fringe pattern, achieving better performance than the original UNet and hNet models in both qualitative and quantitative evaluation.