Dr. Wenqing Su Profile

Dr. Wenqing Su

at Guangdong Univ of Technology

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Proceedings Article | 13 December 2023 Paper

Large depth range dithered binary focusing fringe projection technique

Ji Tan, Xu Wang, Wenqing Su, Zhaoshui He

Proceedings Volume 12942, 1294203 (2023) https://doi.org/10.1117/12.3005606

KEYWORDS: Binary data, Fringe analysis, Phase unwrapping, 3D metrology

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The binary defocusing technique is sensitive to the defocusing degree. The defocusing projection mechanism will introduce high-frequency harmonics at the inappropriate defocused level, leading to limitations in measurement accuracy and depth range. In this paper, a binary-focusing projection technique combining generative adversarial networks is proposed. First, the focusing binary patterns based on error diffusion are projected on the measured surface, and then the captured fringe patterns are input to generative adversarial networks, which achieves sinusoidal correction and optimization for both the focused region and the low-quality defocused region due to its strong image translation ability. Finally, 3D measurement is realized by a phase-shifting algorithm. Compared with the traditional binary defocusing technique, the proposed method is not limited by the defocusing degree and maintains the advantages of high-speed projection, so it can achieve a larger measured depth range and improve measurement accuracy. Simulation and experiments verify the performance of the proposed method.

Proceedings Article | 27 March 2022 Paper

A deep learning-based approach for motion-induced error reduction in fringe projection profilometry

Wenqing Su, Ji Tan, Zhaoshui He

Proceedings Volume 12169, 12169BB (2022) https://doi.org/10.1117/12.2626784

KEYWORDS: Structured light, Neural networks, Phase shifting, Projection systems, Motion measurement

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Fringe projection profilometry (FPP) is one of popular 3D measurement techniques, which can be divided into two categories: Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). Compared to FTP, PSP has been increasingly appealing to researchers due to its merits of higher accuracy and sensitivity. Although PSP works well with the assumption that the object stays quasi-static, it is sensitive to the object motion and causes the motion-induced error in dynamic measurement due to the multi-frame measurement mechanism. However, the multi-frequency phase unwrapping is always utilized to solve the problem of surface discontinuities, which limits the reduction of numbers of projected patterns. Besides, using the high-speed camera will increase the cost of the hardware. Recently, researchers have demonstrated that the phase unwrapping, fringe denoising and dynamic range can be improved with the assistance of deep learning technique. Therefore, in this paper, a deep learning-based method is proposed for motion-induced error reduction. With the aid of strong fitting capability of the neural network, the motion-induced errors can be significantly reduced even under low capture frame rate. The proposed method is experimentally verified on its applicability for dynamic 3D measurement.

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