Deep learning-enabled single-shot fringe projection profilometry with composite coding strategy

In recent years, due to the rapid development of deep learning technology in computer vision, deep learning has gradually penetrated into fringe projection profilometry (FPP) to improve the efficiency of three-dimensional (3D) shape measurement and solve the problem of phase/or depth retrieval accuracy. In order to measure dynamic scenes or high-speed events, the single-shot fringe projection technique, due to its single-frame measurement property that can completely overcome the motion-induced errors of the object, becomes one of the optimal options. In this paper, we introduce a deep learning-enabled single-shot fringe projection profilometry with a composite coding strategy. By combining an FPP physical model-based network architecture with a large dataset, we demonstrate that models generated by training an improved deep convolutional neural network can directly perform high-precision phase retrieval on a single fringe image.