Triple-output phase unwrapping network with a physical prior in fringe projection profilometry

Abstract

Deep learning has been attracting more and more attention in the phase unwrapping of fringe projection profilometry (FPP) in recent years. In order to improve the accuracy of the deep-learning-based unwrapped phase methods from a single fringe pattern, this paper proposes a single-input triple-output neural network structure with a physical prior. In the proposed network, a single-input triple-output network structure is developed to convert the input fringe pattern into three intermediate outputs: the wrapped phase, the fringe order, the coarse unwrapped phase, and the final output high-precision unwrapped phase from the three outputs. Moreover, a new, to the best of our knowledge, loss function is designed to improve the performance of the model using a physical prior about these three outputs in FPP. Numerous experiments demonstrated that the proposed network is able to improve the accuracy of the unwrapped phase, which can also be extended to other deep learning phase unwrapping models.