3D microstructural generation from 2D images of cement paste using generative adversarial networks
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866929593854722048 |
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| author | Zhao, Xin Wang, Lin Li, Qinfei Chen, Heng Liu, Shuangrong Hou, Pengkun Ye, Jiayuan Pei, Yan Wu, Xu Yuan, Jianfeng Gao, Haozhong Yang, Bo |
| author_facet | Zhao, Xin Wang, Lin Li, Qinfei Chen, Heng Liu, Shuangrong Hou, Pengkun Ye, Jiayuan Pei, Yan Wu, Xu Yuan, Jianfeng Gao, Haozhong Yang, Bo |
| contents | Establishing a realistic three-dimensional (3D) microstructure is a crucial step for studying microstructure development of hardened cement pastes. However, acquiring 3D microstructural images for cement often involves high costs and quality compromises. This paper proposes a generative adversarial networks-based method for generating 3D microstructures from a single two-dimensional (2D) image, capable of producing high-quality and realistic 3D images at low cost. In the method, a framework (CEM3DMG) is designed to synthesize 3D images by learning microstructural information from a 2D cross-sectional image. Experimental results show that CEM3DMG can generate realistic 3D images of large size. Visual observation confirms that the generated 3D images exhibit similar microstructural features to the 2D images, including similar pore distribution and particle morphology. Furthermore, quantitative analysis reveals that reconstructed 3D microstructures closely match the real 2D microstructure in terms of gray level histogram, phase proportions, and pore size distribution. The source code for CEM3DMG is available in the GitHub repository at: https://github.com/NBICLAB/CEM3DMG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_01645 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | 3D microstructural generation from 2D images of cement paste using generative adversarial networks Zhao, Xin Wang, Lin Li, Qinfei Chen, Heng Liu, Shuangrong Hou, Pengkun Ye, Jiayuan Pei, Yan Wu, Xu Yuan, Jianfeng Gao, Haozhong Yang, Bo Image and Video Processing Computer Vision and Pattern Recognition Establishing a realistic three-dimensional (3D) microstructure is a crucial step for studying microstructure development of hardened cement pastes. However, acquiring 3D microstructural images for cement often involves high costs and quality compromises. This paper proposes a generative adversarial networks-based method for generating 3D microstructures from a single two-dimensional (2D) image, capable of producing high-quality and realistic 3D images at low cost. In the method, a framework (CEM3DMG) is designed to synthesize 3D images by learning microstructural information from a 2D cross-sectional image. Experimental results show that CEM3DMG can generate realistic 3D images of large size. Visual observation confirms that the generated 3D images exhibit similar microstructural features to the 2D images, including similar pore distribution and particle morphology. Furthermore, quantitative analysis reveals that reconstructed 3D microstructures closely match the real 2D microstructure in terms of gray level histogram, phase proportions, and pore size distribution. The source code for CEM3DMG is available in the GitHub repository at: https://github.com/NBICLAB/CEM3DMG. |
| title | 3D microstructural generation from 2D images of cement paste using generative adversarial networks |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2204.01645 |