Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement
This addresses the problem of accurate and efficient 3D imaging in complex scenes for applications like industrial inspection, though it is incremental as it builds on prior SPU techniques.
The paper tackles the challenge of robust spatial phase unwrapping (SPU) in fringe projection profilometry for 3D measurement by proposing a hybrid method combining deep learning and traditional path-following, which demonstrates better robustness, interpretability, and generality than existing methods in experiments on real datasets.
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.