  In terms of 3D imaging speed and system cost, the single-camera system
projecting single-frequency patterns is the ideal option among all proposed
Fringe Projection Profilometry (FPP) systems. This system necessitates a robust
spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a
challenge in complex scenes. Quality-guided SPU algorithms need more efficient
ways to identify the unreliable points in phase maps before unwrapping.
End-to-end deep learning SPU methods face generality and interpretability
problems. This paper proposes a hybrid method combining deep learning and
traditional path-following for robust SPU in FPP. This hybrid SPU scheme
demonstrates better robustness than traditional quality-guided SPU methods,
better interpretability than end-to-end deep learning scheme, and generality on
unseen data. Experiments on the real dataset of multiple illumination
conditions and multiple FPP systems differing in image resolution, the number
of fringes, fringe direction, and optics wavelength verify the effectiveness of
the proposed method.
