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

Jiaye J Wang; Yuzhen Zhang

doi:10.1117/12.2606719

24 November 2021 High-speed 3D shape measurement from noisy fringe images using deep learning

Jiaye J Wang, Yuzhen Zhang

Author Affiliations +

Proceedings Volume 12065, AOPC 2021: Optical Sensing and Imaging Technology; 120652R (2021) https://doi.org/10.1117/12.2606719
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

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.

Citation Download Citation

Jiaye J Wang and Yuzhen Zhang "High-speed 3D shape measurement from noisy fringe images using deep learning", Proc. SPIE 12065, AOPC 2021: Optical Sensing and Imaging Technology, 120652R (24 November 2021); https://doi.org/10.1117/12.2606719

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