High-precision binocular fringe reflectometry based on normal vector consistency

Yuehua Li; Bin Cui; Zhijun Zhao; Deng Li; Jingbo Zhou

doi:10.1117/12.3032209

3 June 2024 High-precision binocular fringe reflectometry based on normal vector consistency

Yuehua Li, Bin Cui, Zhijun Zhao, Deng Li, Jingbo Zhou

Proceedings Volume 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024); 131701K (2024) https://doi.org/10.1117/12.3032209
Event: Third International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 2024, Wuhan, China

Abstract

Modern manufacturing industry has a strong measurement demand of specular surfaces. Fringe reflection method has the advantages of simple structure, low cost, high accuracy, and is becoming more and more popular for 3D measurement of these surfaces. Most of previous methods achieve the fringe reflection measurement using a single camera, and suffer the problem of gradient-height ambiguity. Here, a binocular fringe reflectometry method is proposed with the surface normal is obtained through normal vector consistency of the measured surface. To enhance the computation efficiency, a polar constraint and bisection method are also developed to get the matching points on the two camera images. After that the normal vector and the gradient distribution can be calculated. The 3D measurement result can be obtained according to integral reconstruction algorithm subsequently. To verify the effectiveness of the method, measurements were performed on a spherical mirror. Compared with the result from interferometer, the deviation of peak to valley value is 314.3 nm, and the root mean square value is 52.8 nm, which indicates high accuracy of the method.

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

Citation Download Citation

Yuehua Li, Bin Cui, Zhijun Zhao, Deng Li, and Jingbo Zhou "High-precision binocular fringe reflectometry based on normal vector consistency", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 131701K (3 June 2024); https://doi.org/10.1117/12.3032209

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