论文标题
使用生成对抗网络从水泥糊的2D图像中产生3D微结构的生成
3D microstructural generation from 2D images of cement paste using generative adversarial networks
论文作者
论文摘要
建立现实的三维（3D）微观结构是研究硬化水泥糊的微观结构发展的关键步骤。但是，获得水泥的3D微结构图像通常涉及高成本和质量妥协。本文提出了一种基于生成的对抗网络的方法，用于从单个二维（2D）图像生成3D微观结构，能够以低成本生产高质量和现实的3D图像。在该方法中，通过从2D横截面图像中学习微观结构信息来综合3D图像框架（CEM3DMG）。实验结果表明，CEM3DMG可以生成大尺寸的现实3D图像。视觉观察证实，生成的3D图像表现出与2D图像相似的微结构特征，包括相似的孔隙分布和颗粒形态。此外，定量分析表明，根据灰度直方图，相位比例和孔径分布，重建的3D微结构与真实的2D微结构非常匹配。 CEM3DMG的源代码可在GitHub存储库中获得：https：//github.com/nbiclab/cem3dmg。
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.
