深度学习的应用简化了数字条纹投影三维测量的过程，在传统数字条纹投影三维测量技术条纹投影、相位计算、相位展开、相位深度映射的流程中，研究者们已经成功证明了前三个环节以及整个流程结合深度神经网络的可行性。基于深度学习，PDNet (Phase to Depth Network)神经网络模型被提出，用于绝对相位到深度的映射。结合多阶段深度学习单帧条纹投影三维测量方法，通过分阶段学习方式依次获得物体的绝对相位与深度信息。实验结果表明，PDNet能较准确地测量出物体的深度信息，深度学习应用于相位深度映射步骤具有可行性。并且，相较于直接从条纹图像到三维形貌的单阶段深度学习单帧条纹投影三维测量方法，多阶段深度学习单帧条纹投影三维测量方法可以明显提升测量精度，仅需单帧条纹图像输入即可获得毫米级测量精度，且能适应具有复杂形貌物体的三维测量。

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.