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.

中文翻译：

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使用生成对抗网络从水泥浆的 2D 图像生成 3D 微观结构


建立逼真的三维 （3D） 微观结构是研究硬化水泥浆微观结构发展的关键步骤。然而，获取水泥的 3D 微观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对抗网络的方法，用于从单个二维 （2D） 图像生成 3D 微结构，能够以低成本生成高质量和逼真的 3D 图像。该方法设计了一个框架 （CEM3DMG） 通过从 2D 横截面图像中学习微观结构信息来合成 3D 图像。实验结果表明，CEM3DMG 可以生成逼真的大尺寸 3D 图像。目视观察证实，生成的 3D 图像表现出与 2D 图像相似的微观结构特征，包括相似的孔隙分布和颗粒形态。此外，定量分析表明，重建的 3D 微观结构在灰度直方图、相位比例和孔径分布方面与真实的 2D 微观结构非常匹配。