End-to-end single-shot composite fringe projection profilometry based on deep learning

Yixuan Li; Jiaming Qian; Shijie Feng; Qian Chen; Chao Zuo

doi:10.1117/12.2587728

15 January 2021 End-to-end single-shot composite fringe projection profilometry based on deep learning

Yixuan Li, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

Proceedings Volume 11761, Fourth International Conference on Photonics and Optical Engineering; 117611T (2021) https://doi.org/10.1117/12.2587728
Event: Fourth International Conference on Photonics and Optical Engineering, 2020, Xi'an, China

Abstract

Using a single fringe image to complete the dynamic absolute 3D reconstruction has become a tremendous challenge and an eternal pursuit for researchers. In fringe projection profilometry (FPP), although many methods can achieve high-precision 3D reconstruction from simple system architecture via appropriate encoding ways, they usually cannot retrieve the absolute 3D information of objects with complex surfaces through only a single fringe pattern. In this work, we develop a single-frame composite fringe encoding approach and use a deep convolutional neural network to retrieve the absolute phase of the object from this composite pattern end to- end. The proposed method can directly obtain spectrum-aliasing-free phase information and robust phase unwrapping from single-frame compound input through extensive data learning. Experiments have demonstrated that the proposed deep-learning-based approach can achieve absolute phase retrieval using a single image.

Citation Download Citation

Yixuan Li, Jiaming Qian, Shijie Feng, Qian Chen, and Chao Zuo "End-to-end single-shot composite fringe projection profilometry based on deep learning", Proc. SPIE 11761, Fourth International Conference on Photonics and Optical Engineering, 117611T (15 January 2021); https://doi.org/10.1117/12.2587728

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available