High-speed 3D shape measurement from noisy fringe images using deep learning

Abstract

High-speed three-dimensional (3D) shape measurement has become a very important technology in industrial manufacturing, motion detection and other scientific research. Although there are some methods to measure 3D surface patterns, it is still difficult to accurately measure the rapidly changing 3D high-speed scenes. Multi-frequency phase unwrapping usually uses a combination of noisy fringe images with different fringe frequencies for phase unwrapping, which has high accuracy and reliability. Benefiting from the success of deep learning in the field of computer vision in recent years, we combine multi-frequency phase-shifting and phase unwrapping with deep learning, and propose the high-speed 3D shape measurement from noisy fringe images using deep learning. Compared with traditional methods, this method can achieve more convenient and robust phase retrieval at high speed. Based on a good training model, the deep learning neural network can directly achieve the corresponding high-quality phase results after extensive learning of the data set collected at high speed. The experimental results demonstrate that this method can achieve 3D shape of the measured object with an accuracy of about 51μm at the camera frame rate of 700 frames per second.