Digital twin-trained deep convolutional neural networks for fringe analysis

High-speed three-dimensional (3D) fringe projection profilometry (FPP) is widely used in many fields. Recently, researchers have successfully tested the feasibility of performing fringe analysis using deep convolutional networks (CNN). However, the existing methods require tremendous real-world scanning trials for model training, which is not trivial. In this work, we propose a framework to establish the digital twin of a real-world system in a virtual environment and a process to automatically generate 3D training data. Experiments are conducted to demonstrated that a physical system can adopt the CNN trained in the virtual environment to perform accurate real-world 3D shape measurements.