Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

中文翻译：

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用于边缘投影3D成像的深度卷积神经网络相位展开。

相位展开是条纹投影3D成像中非常重要的一步。在本文中，我们提出了一种用于精确相位展开的新神经网络，以解决条纹投影3D成像中的特殊需求。代替直接用整数标记包裹的阶段，建议使用具有相同网络配置的两步训练过程。第一步，训练网络（网络I）在包装阶段仅标记四个关键特征。在第二步中，训练具有相同配置的另一个网络（网络II）以标记包裹的相位段。优点是，包裹相位的维数可以比训练数据的维数大得多，并且可以正确解开具有严重高斯噪声的相位。