Correction of grating patterns for high dynamic range 3D measurement based on deep learning

Fringe projection profilometry (FPP) technology, renowned for its stable and high-precision characteristics, is widely employed in three-dimensional surface measurements of objects. Whether utilizing deep learningbased methods or traditional multi-frequency, multi-step fringe analysis techniques, both require acquiring highquality stripe patterns modulated by the three-dimensional surface of the object. However, the limited dynamic range of cameras makes it difficult to capture effective fringe information in a single exposure, and while multiexposure methods can address this issue, they are inefficient. To address this, this study proposes an end-to-end neural network approach for generating high dynamic range (HDR) fringe patterns from projected gratings. Additionally, an end-to-end network is employed to solve the fringe phase. Experimental results demonstrate that this method significantly improves fringe pattern recovery on metallic surfaces with overexposed or underexposed regions. On a high dynamic range reflectivity dataset, the method achieved a phase error of 0.02072, successfully reconstructing 3D objects with only 8.3% of the time required by the 12-step Phase Shifting Profilometry (PSP) method. Furthermore, on standard spherical and planar objects, the method achieved a radius accuracy of 53.1 μm and flatness accuracy of 61.7 μm, demonstrating effective measurement precision without the need for additional steps. This method is effective for both high dynamic range reflective and non-high dynamic range reflective objects.