首页|Wavelet based deep learning for depth estimation from single fringe pattern of fringe projection profilometry
Wavelet based deep learning for depth estimation from single fringe pattern of fringe projection profilometry
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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 atten-tion 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 computa-tional 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,achiev-ing better performance than the original UNet and hNet models in both qualitative and quantitative evaluation.
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