3D reconstruction of single-image based on deep learning and fringe projection profilometry

Xu Li; Zhisen Yang; Mingyi Xing; Hualin Yang; Qiushuang Zhang

doi:10.1117/12.2691227

6 November 2023 3D reconstruction of single-image based on deep learning and fringe projection profilometry

Xu Li, Zhisen Yang, Mingyi Xing, Hualin Yang, Qiushuang Zhang

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 129212Y (2023) https://doi.org/10.1117/12.2691227
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

Due to the complicated realization process, the traditional three-dimensional (3D) reconstruction method of structured light gradually fails to meet the needs of actual production and complex scenes. The combination of fringe projection profilometry and deep learning effectively improves the situation. Classical neural network models have gradually shown their unique advantages in the field of 3D reconstruction. Although the existing reconstruction methods have been improved in different aspects, they still have the problems of complex data set production and low reconstruction accuracy, so they are difficult to be applied to the actual 3D measurement. On this basis, a virtual 3D measurement simulation system based on fringe projection profiling is built to generate batch training data, simplifying the actual data collection process. And used the traditional fringe projection profilometry to rebuild the model as the subsequent ground truth, to verify the effectiveness of the virtual data set. In this paper, the phase information is taken as the target, a multi-scale feature fusion convolution neural network is used to transform a single fringe image into multiple single frequency phase shift images, then the single frequency phase shift images used as input to get the fringe order. In this way, 3D reconstruction of complex objects can be realized, which simplifies the complicated calculation process of traditional methods. After a large number of experiments, the proposed method is proved to be more accurate and efficient than the existing methods.

Citation Download Citation

Xu Li, Zhisen Yang, Mingyi Xing, Hualin Yang, and Qiushuang Zhang "3D reconstruction of single-image based on deep learning and fringe projection profilometry", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 129212Y (6 November 2023); https://doi.org/10.1117/12.2691227

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KEYWORDS

3D modeling

Deep learning

Image restoration

Phase shifts

3D projection

Fringe analysis

Cameras

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