Deep learning-based single-shot spatial frequency multiplexing composite fringe projection profilometry

Abstract

Fringe projection profilometry (FPP) has been widely used in high-speed, dynamic, real-time three-dimensional (3D) shape measurements. How to recover the high-accuracy and high-precision 3D shape information by a single fringe pattern is our long-term goal in FPP. Traditional single-shot fringe projection measurement methods are difficult to achieve high-precision 3D shape measurement of isolated and complex surface objects due to the influence of object surface reflectivity and spectral aliasing. In order to break through the physical limits of the traditional methods, we apply deep convolutional neural networks to single-shot fringe projection profilometry. By combining physical models and data-driven, we demonstrate that the model generated by training an improved U-Net network can directly perform high-precision and unambiguous phase retrieval on a single-shot spatial frequency multiplexing composite fringe image while avoiding spectrum aliasing. Experiments show that our method can retrieve high-quality absolute 3D surfaces of objects only by projecting a single composite fringe image.