High-speed fringe projection profilometry based on convolutional neural network

Aiming at the serious noise and low signal-to-noise ratio of the fringe images obtained by industrial cameras under high-speed projection, a high-speed fringe projection measurement method based on convolutional neural network is proposed, which can achieve high-quality phase recovery and high-precision three-dimensional reconstruction in high-speed scenes. Using the designed convolutional neural network, the noise fringe images obtained at high frame rate and the wrapped phase images recovered by the traditional 12-step phase-shifting method at low frame rate are input into the convolutional neural network for training. After learning the mapping relationship between a large number of noise fringe images in the data set and the corresponding high-quality wrapped phase, a trained network model is obtained. And using this model, the high-quality wrapped phase information can be directly recovered from the input noise fringe images. The experiment results demonstrate that the method proposed in this paper can achieve with an accuracy of about 32 mu m through three noise fringe images at the camera frame rate of 700 frames per second.