Single-frame grating pattern phase recovery for high dynamic range 3D measurement based on deep learning

Fringe Projection Profilometry (FPP) faces challenges with objects of varying surface reflectivity, as projected light can exceed the camera’s dynamic range, hindering effective fringe capture. Current solutions using repeated projections with varying exposures increase measurement time, limiting real-time applicability. This study validates deep neural networks that transform traditional multi-frequency, multi-step, multi-exposure methods into a single-step, multi-exposure format, significantly reducing measurement time while maintaining accuracy. Experimental results demonstrate that deep learning methods can effectively extract phase information from modulated fringe images, unwrap it, and reconstruct 3D point clouds. On high-reflectivity metal datasets, the accuracy of the deep learning approach closely matches that of the traditional six-step method, while using only 16.7% of the time. For standard objects, the accuracy reaches up to 60 microns. These findings confirm that various deep learning methods can efficiently resolve phase information in modulated fringe patterns, significantly enhancing measurement speed.