Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement

Fringe projection profilometry (FPP) has become a more prevalently adopted technique in intelligent manufacturing, defect detection, and some other important applications. In FPP, efficiently recovering the absolute phase has always been a great challenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional patterns, which maximizes the efficiency of the retrieval of the absolute phase. Inspired by recent successes of deep learning for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies phase retrieval, geometric constraints, and phase unwrapping into a comprehensive framework. Driven by extensive training datasets, the neural network can gradually “learn” to transfer one high-frequency fringe pattern into the “physically meaningful” and “most likely” absolute phase, instead of “step by step” as in conventional approaches. Based on the properly trained framework, high-q...