Single-frame grating pattern phase recovery for high dynamic range 3D measurement based on deep learning

Hao Wang; Ziyang Huang; Zhongyuan Zhang; Can Cui; Chaobo Zhang; Xiaojun Liang

doi:10.1117/12.3036256

22 November 2024 Single-frame grating pattern phase recovery for high dynamic range 3D measurement based on deep learning

Hao Wang, Ziyang Huang, Zhongyuan Zhang, Can Cui, Chaobo Zhang, Xiaojun Liang

Author Affiliations +

Proceedings Volume 13239, Optoelectronic Imaging and Multimedia Technology XI; 1323918 (2024) https://doi.org/10.1117/12.3036256
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Conference Poster

Abstract

Fringe Projection Profilometry (FPP) faces challenges with objects of varying surface reflectivity, as projected light can exceed the camera’s dynamic range, hindering effective fringe capture. Current solutions using repeated projections with varying exposures increase measurement time, limiting real-time applicability. This study validates deep neural networks that transform traditional multi-frequency, multi-step, multi-exposure methods into a single-step, multi-exposure format, significantly reducing measurement time while maintaining accuracy. Experimental results demonstrate that deep learning methods can effectively extract phase information from modulated fringe images, unwrap it, and reconstruct 3D point clouds. On high-reflectivity metal datasets, the accuracy of the deep learning approach closely matches that of the traditional six-step method, while using only 16.7% of the time. For standard objects, the accuracy reaches up to 60 microns. These findings confirm that various deep learning methods can efficiently resolve phase information in modulated fringe patterns, significantly enhancing measurement speed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Wang, Ziyang Huang, Zhongyuan Zhang, Can Cui, Chaobo Zhang, and Xiaojun Liang "Single-frame grating pattern phase recovery for high dynamic range 3D measurement based on deep learning", Proc. SPIE 13239, Optoelectronic Imaging and Multimedia Technology XI, 1323918 (22 November 2024); https://doi.org/10.1117/12.3036256

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