高精度条纹投影轮廓术中的逐点后验相位估计
Point-wise posteriori phase estimation in high-precision fringe projection profilometry
摘要：
在条纹投影轮廓术中，非正弦条纹的高次谐波信息会导致相位估计的误差。为了解决这一问题，本文提出了一种基于深度学习技术的逐点后验相位估计（PWPPE）方法。利用前向神经网络模型，构建了多个灰度值与相位正/余弦值之间的复杂非线性映射关系。在模型训练后，它可以估计每个像素位置的相位值，精度高于逐点最小二乘（PWLS）方法。为了进一步验证该方法的有效性，测量了一个面具，分别采用传统的PWLS方法和PWPPE方法。对比结果表明，传统方法存在周期性相位误差，而PWPPE方法能有效消除非正弦条纹引起的相位误差。
英文摘要：
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.
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