Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement

TL;DR

This paper introduces a hybrid deep learning and traditional algorithm for spatial phase unwrapping in 3D imaging, improving robustness, interpretability, and generality across various complex scenes and system configurations.

Contribution

It proposes a novel hybrid SPU method that combines deep learning with traditional path-following, addressing robustness and interpretability issues in complex 3D measurement scenes.

Findings

Outperforms traditional quality-guided SPU in robustness

Demonstrates better interpretability than end-to-end deep learning methods

Shows generality across different imaging conditions and system setups

Abstract

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.