  Fringe projection profilometry (FPP) is one of the most popular
three-dimensional (3D) shape measurement techniques, and has becoming more
prevalently adopted in intelligent manufacturing, defect detection and some
other important applications. In FPP, how to efficiently recover 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 fringe patterns, which maximizes the
efficiency of the retrieval of absolute phase. Inspired by the recent success
of deep learning technologies for phase analysis, we demonstrate that deep
learning can be an effective tool that organically unifies the phase retrieval,
geometric constraints, and phase unwrapping steps into a comprehensive
framework. Driven by extensive training dataset, the neutral network can
gradually "learn" how to transfer one high-frequency fringe pattern into the
"physically meaningful", and "most likely" absolute phase, instead of "step by
step" as in convention approaches. Based on the properly trained framework,
high-quality phase retrieval and robust phase ambiguity removal can be achieved
based on only single-frame projection. Experimental results demonstrate that
compared with traditional SPU, our method can more efficiently and stably
unwrap the phase of dense fringe images in a larger measurement volume with
fewer camera views. Limitations about the proposed approach are also discussed.
We believe the proposed approach represents an important step forward in
high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement
for complicated object from a single fringe pattern.
