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  4. Vol 6, Issue 3, 2022

Phase Completion for Fringe Projection Profiler Based on Neural Networks

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DOI: 10.23977/acss.2022.060305 | Downloads: 20 | Views: 733

Author(s)

Ziyu Yin 1, Junzheng Li 2

Affiliation(s)

1 Department of Game, Software Engineering Institute of Guangzhou, Guangzhou, China
2 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Corresponding Author

Ziyu Yin

ABSTRACT

Fringe projection profiler (FPP) measures the geometry of the target surface by projecting the pre-modulated stripe map onto the surface, and then capture the phase map with a camera. However, the inaccurate exposure or the characteristics of the surface reflectance may influence the imaging quality of the phase map, leaving some over-exposure and under-exposure regions. Addressing to this problem, this paper propose to apply a neural network to complete the phase map. Firstly, we propose a synthetic dataset to simulate the phase map of the inaccurate exposure regions, based on a physical rendering model. After that, we implement a transformer neural network to complete the missing phase information. Experiments show that the proposed neural network can complete the missing information from its neighbouring information, and provide precise completion results.

KEYWORDS

fringe projection profiler (FPP), physical rendering, neural network, optical measurement

CITE THIS PAPER

Ziyu Yin, Junzheng Li, Phase Completion for Fringe Projection Profiler Based on Neural Networks . Advances in Computer, Signals and Systems (2022) Vol. 6: 35-42. DOI: http://dx.doi.org/10.23977/acss.2022.060305.

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