Deep learning-enabled invalid-point removal for spatial phase unwrapping of 3D measurement

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

Automated summary

This paper proposes a hybrid spatial phase unwrapping method that combines deep learning-enabled invalid-point removal and traditional path-following. The method demonstrates better robustness than traditional quality-guided methods, better interpretability than end-to-end deep learning schemes, and generality on unseen data. Experiments on a real dataset validate the effectiveness of the proposed method.