Structured light three-dimensional reconstruction method based on GFU-Net network

Yurong Cao; Yongjian Zhu; Likang Yang; Bo Ouyang

doi:10.1117/12.3026562

27 March 2024 Structured light three-dimensional reconstruction method based on GFU-Net network

Yurong Cao, Yongjian Zhu, Likang Yang, Bo Ouyang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310517 (2024) https://doi.org/10.1117/12.3026562
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In streak projection profilometry, how to reconstruct the 3D shape of an object quickly and accurately from a single streak pattern has been an important research area. Although the CNN-based fringe-to-depth method can directly reconstruct 3D from a single fringe, its accuracy is currently not as good as the traditional phase-shift technique. In order to improve the accuracy of 3D reconstruction, this paper proposes a U-Net-based global feature fusion network (GFU-Net), which introduces a global feature fusion module to fuse the global context information, and uses a feature fusion upsampling module to recover the spatial detail information, which solves the problem that it is difficult to accurately obtain the depth information of an object from a single fringe map. The experimental results show that the method proposed in this paper decreases the RMSE by 17.8% compared to the U-Net network, and the 3D reconstruction accuracy is higher and the error is smaller, which verifies the effectiveness and robustness of the method.

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

Citation Download Citation

Yurong Cao, Yongjian Zhu, Likang Yang, and Bo Ouyang "Structured light three-dimensional reconstruction method based on GFU-Net network", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310517 (27 March 2024); https://doi.org/10.1117/12.3026562

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KEYWORDS

3D modeling

Feature fusion

Data modeling

Fringe analysis

Education and training

Structured light

Convolution

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