Generating 3D structures from a 2D slice with GAN-based dimensionality expansion (2102.07708v1)

Abstract: Generative adversarial networks (GANs) can be trained to generate 3D image data, which is useful for design optimisation. However, this conventionally requires 3D training data, which is challenging to obtain. 2D imaging techniques tend to be faster, higher resolution, better at phase identification and more widely available. Here, we introduce a generative adversarial network architecture, SliceGAN, which is able to synthesise high fidelity 3D datasets using a single representative 2D image. This is especially relevant for the task of material microstructure generation, as a cross-sectional micrograph can contain sufficient information to statistically reconstruct 3D samples. Our architecture implements the concept of uniform information density, which both ensures that generated volumes are equally high quality at all points in space, and that arbitrarily large volumes can be generated. SliceGAN has been successfully trained on a diverse set of materials, demonstrating the widespread applicability of this tool. The quality of generated micrographs is shown through a statistical comparison of synthetic and real datasets of a battery electrode in terms of key microstructural metrics. Finally, we find that the generation time for a $10^8$ voxel volume is on the order of a few seconds, yielding a path for future studies into high-throughput microstructural optimisation.

• The paper introduces SliceGAN, a GAN architecture that generates 3D structures from 2D micrographs, reducing the need for complex 3D training datasets.
• It employs a novel slicing mechanism and uniform information density in convolutional operations to maintain consistent image quality across generated volumes.
• Validation on diverse materials, including synthetic grains and battery electrodes, demonstrates its effectiveness in preserving key microstructural characteristics for simulation.

An Overview of "Generating 3D Structures from a 2D Slice with GAN-based Dimensionality Expansion"

The paper introduces SliceGAN, a generative adversarial network (GAN) architecture designed to synthesize 3D volumetric data from 2D micrograph images. Conventional GANs necessitate 3D training data, which is often difficult and resource-intensive to obtain. SliceGAN bypasses this limitation by using readily available 2D imaging data to reconstruct 3D structures, with a focus on material microstructure generation. This method is particularly beneficial in fields requiring high-resolution and extensive volumetric datasets for simulations, such as in material science for analyzing properties dependent on microstructures.

Key Contributions

The paper details several contributions to the field of generative modeling:

1. New GAN Architecture: SliceGAN's architecture addresses the fundamental challenge of generating 3D data from 2D images by incorporating a slicing mechanism. This approach slices generated volumes along three orthogonal planes, aligning with the isotropic nature of the input 2D images.
2. Uniform Information Density: The authors solve the problem of low-quality image regions, typically found at the edges, by implementing uniform information density in the transpose convolutional operations. This ensures consistent quality across generated images.
3. Demonstration on Diverse Materials: SliceGAN successfully reconstructs various materials, from synthetic to complex battery electrodes, validated through statistical measures, demonstrating its wide applicability.

Methodology and Results

The SliceGAN model employs a novel approach wherein the generator constructs a complete 3D volume. This volume is then sliced into 2D images before being input into the discriminator, overcoming the traditional dimensionality mismatch. In practice, the authors demonstrate the effectiveness of SliceGAN across multiple material types, including isotropic materials like synthetic grains and anisotropic structures such as fiber-reinforced composites. The reconstructed 3D volumes were statistically compared to real datasets, proving the model's capability in preserving essential microstructural characteristics crucial for simulations.

Implications and Future Directions

The implications of SliceGAN are substantial in material science, where 3D imaging plays a pivotal role. Its ability to quickly and efficiently generate high-fidelity 3D images facilitates advanced simulations for material behavior analysis under various conditions. Furthermore, SliceGAN's flexibility suggests potential integration with other GAN variants, such as conditional GANs for generating labeled structures and transfer learning to expedite model training.

The paper opens pathways for the continued evolution of machine learning tools in material characterization, offering prospects for large-scale applications, rapid microstructural optimization, and a deeper understanding of 3D properties emerging from 2D samples. Future research may involve refining the generator's architecture for enhanced feature capture and expanding its applicability to other domains necessitating high-throughput 3D image synthesis.