Digital twin-trained deep convolutional neural networks for fringe analysis

Yi Zheng; Beiwen Li

doi:10.1117/12.2582823

5 March 2021 Digital twin-trained deep convolutional neural networks for fringe analysis

Yi Zheng, Beiwen Li

Proceedings Volume 11698, Emerging Digital Micromirror Device Based Systems and Applications XIII; 116980K (2021) https://doi.org/10.1117/12.2582823
Event: SPIE OPTO, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Yi Zheng and Beiwen Li "Digital twin-trained deep convolutional neural networks for fringe analysis", Proc. SPIE 11698, Emerging Digital Micromirror Device Based Systems and Applications XIII, 116980K (5 March 2021); https://doi.org/10.1117/12.2582823

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