  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.
