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Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model
Characterizing the physical properties of clay and cementitious materials matters across many fields, from materials science to geological waste disposal. Property simulation typically calls for 3D imaging, which is expensive, not always accessible, and technically limited for certain materials. Recent progress in deep generative models offers a way around this, reconstructing 3D volumes from the more easily acquired 2D images. Among GAN-based methods for 3D microstructure generation, SliceGAN has shown strong results for homogeneous isotropic and anisotropic systems. It struggles, however, to
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Evidence & attribution
- arXiv · Artificial Intelligence · 2026-09-17T13:21:48.000Z
- arXiv · AI, language, vision and robotics · 2026-09-17T13:21:48.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.