Stereo phase unwrapping using deep learning for single-shot absolute 3D shape measurement

In fringe projection profilometry (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 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 dataset, the properly trained neural network can achieve high-quality phase retrieval and robust phase ambiguity removal from only single-frame projection. Experimental results demonstrate that compared with conventional SPU, our deep-learning-based approach can more efficiently and robustly unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views.