The application of deep learning has simplified the process of 3D measurement of digital fringe projection. In the process of fringe projection, phase calculation, phase unwrapping, and phase-depth mapping of traditional digital fringe projection 3D measurement technology, researchers have successfully demonstrated the feasibility of combining the first three stages and the entire process with deep neural networks. Based on deep learning, the Phase to Depth Network (PDNet) was proposed to achieve the map from absolute phase to depth. Combined with multi-stage deep learning based single-frame fringe projection 3D measurement method, the absolute phase and depth information of the object were obtained by deep learning in stages. The experimental results show that the PDNet can measure the depth information of the object comparatively accurately, and the application of deep learning is feasible in the phase-height mapping stage. And compared with the single-stage deep learning based single-frame fringe projection 3D measurement method that directly maps from the fringe image to the three-dimensional topography information, multi-stage deep learning based single-frame fringe projection 3D measurement method can significantly improve the measurement accuracy, which only require a single fringe input to obtain millimeter-level measurement accuracy, and it can adapt to 3D measurement of objects with complex surfaces.