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Computational imaging

Escape from flatland

Nature Machine Intelligence volume 3, pages 277–278 (2021)Cite this article

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3D image reconstruction is important for the understanding of materials and their function in devices. A generative adversarial network architecture reconstructs 3D materials microstructures from 2D images.

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Fig. 1: SliceGAN architecture.

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Authors and Affiliations

  1. Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, Amiens, France

    Alejandro A. Franco

  2. Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, Amiens, France

    Alejandro A. Franco

  3. ALISTORE-European Research Institute, FR CNRS 3104, Hub de l’Energie, Amiens, France

    Alejandro A. Franco

  4. Institut Universitaire de France, Paris, France

    Alejandro A. Franco

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  1. Alejandro A. Franco
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Correspondence to Alejandro A. Franco.

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Franco, A.A. Escape from flatland. Nat Mach Intell 3, 277–278 (2021). https://doi.org/10.1038/s42256-021-00334-x

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