Point-wise posteriori phase estimation in high-precision fringe projection profilometry

TL;DR

This paper introduces a deep learning-based point-wise posterior phase estimation method for fringe projection profilometry, significantly reducing phase errors caused by non-sinusoidal fringes and improving measurement accuracy.

Contribution

The paper proposes a novel deep learning approach for phase estimation that outperforms traditional methods in handling non-sinusoidal fringe distortions.

Findings

The proposed PWPPE method reduces phase errors compared to traditional PWLS.

Experimental results on face mask measurement validate the effectiveness of the method.

PWPPE effectively eliminates periodic phase errors caused by non-sinusoidal fringes.

Abstract

In fringe projection profilometry, the high-order harmonics information of non-sinusoidal fringes will lead to errors in the phase estimation. In order to solve this problem, a point-wise posterior phase estimation (PWPPE) method based on deep learning technique is proposed in this paper. The complex nonlinear mapping relationship between the multiple gray values and the sine / cosine value of the phase is constructed by using the feedforward neural network model. After the model training, it can estimate the phase values of each pixel location, and the accuracy is higher than the point-wise least-square (PWLS) method. To further verify the effectiveness of this method, a face mask is measured, the traditional PWLS method and the proposed PWPPE method are employed, respectively. The comparison results show that the traditional method is with periodic phase errors, while the proposed PWPPE method can effectively eliminate such phase errors caused by non-sinusoidal fringes.