Three-dimensional shape measurement based on deep learning

Suzhen Zheng; Jinke Dai

doi:10.1117/12.3000601

24 November 2023 Three-dimensional shape measurement based on deep learning

Suzhen Zheng, Jinke Dai

Proceedings Volume 12935, Fourteenth International Conference on Information Optics and Photonics (CIOP 2023); 129350E (2023) https://doi.org/10.1117/12.3000601
Event: Fourteenth International Conference on Information Optics and Photonics (CIOP 2023), 2023, Xi’an, China

Abstract

In order to avoid the complicated phase with height mapping relationship calibration and phase unwrapping process of traditional 3D surface measurement methods, a three-dimensional surface measurement method based on deep learning was proposed. By sampling the rotating fringe pattern, data samples of different directions of the object to be measured are obtained as the training set. After the model is trained well, the mapping relationship between the deformed fringe pattern and the height of the measured object can be directly obtained to realize the 3D surface shape measurement of the object to be measured. The computer simulation and experiment show that the method is effective.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Suzhen Zheng and Jinke Dai "Three-dimensional shape measurement based on deep learning", Proc. SPIE 12935, Fourteenth International Conference on Information Optics and Photonics (CIOP 2023), 129350E (24 November 2023); https://doi.org/10.1117/12.3000601

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