Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data

• URL: http://arxiv.org/abs/2310.18523v1
• Date: Fri, 27 Oct 2023 22:49:08 GMT
• Title: Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data
• Authors: Lukas Fuchs, Tom Kirstein, Christoph Mahr, Orkun Furat, Valentin Baric, Andreas Rosenauer, Lutz Maedler, Volker Schmidt
• Abstract summary: A parametric 3D model is presented, from which a wide spectrum of virtual hetero-aggregates can be generated. The virtual structures are passed to a physics-based simulation tool in order to generate virtual scanning transmission electron microscopy (STEM) images. Convolutional neural networks are trained to predict 3D structures of hetero-aggregates from 2D STEM images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The structural characterization of hetero-aggregates in 3D is of great interest, e.g., for deriving process-structure or structure-property relationships. However, since 3D imaging techniques are often difficult to perform as well as time and cost intensive, a characterization of hetero-aggregates based on 2D image data is desirable, but often non-trivial. To overcome the issues of characterizing 3D structures from 2D measurements, a method is presented that relies on machine learning combined with methods of spatial stochastic modeling, where the latter are utilized for the generation of synthetic training data. This kind of training data has the advantage that time-consuming experiments for the synthesis of differently structured materials followed by their 3D imaging can be avoided. More precisely, a parametric stochastic 3D model is presented, from which a wide spectrum of virtual hetero-aggregates can be generated. Additionally, the virtual structures are passed to a physics-based simulation tool in order to generate virtual scanning transmission electron microscopy (STEM) images. The preset parameters of the 3D model together with the simulated STEM images serve as a database for the training of convolutional neural networks, which can be used to determine the parameters of the underlying 3D model and, consequently, to predict 3D structures of hetero-aggregates from 2D STEM images. Furthermore, an error analysis is performed to evaluate the prediction power of the trained neural networks with respect to structural descriptors, e.g. the hetero-coordination number.

Related papers

• A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation [4.169915659794567]
  The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective. A novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed. The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.
  arXiv  Detail & Related papers  (2024-02-24T13:42:34Z)
• Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis [17.920305227880245]
  Our paper formulates triple vision tasks in a consistent manner using approximate analysis-by-synthesis. We show that our analysis-by-synthesis is much more robust than conventional neural networks when evaluated on real-world images.
  arXiv  Detail & Related papers  (2023-05-31T18:45:02Z)
• NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions [97.27105725738016]
  integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs) has transformed 3D-aware generation from single-view images. We propose a simple and effective method, based on re-using the well-disentangled latent space of a pre-trained NeRF-GAN in a pose-conditioned convolutional network to directly generate 3D-consistent images corresponding to the underlying 3D representations.
  arXiv  Detail & Related papers  (2023-03-22T18:59:48Z)
• RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects [68.85305626324694]
  Ray-marching in Camera Space (RiCS) is a new method to represent the self-occlusions of foreground objects in 3D into a 2D self-occlusion map. We show that our representation map not only allows us to enhance the image quality but also to model temporally coherent complex shadow effects.
  arXiv  Detail & Related papers  (2022-05-14T05:35:35Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• Scene Synthesis via Uncertainty-Driven Attribute Synchronization [52.31834816911887]
  This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches.
  arXiv  Detail & Related papers  (2021-08-30T19:45:07Z)
• Generating 3D structures from a 2D slice with GAN-based dimensionality expansion [0.0]
  Generative adversarial networks (GANs) can be trained to generate 3D image data, which is useful for design optimisation. We introduce a generative adversarial network architecture, SliceGAN, which is able to synthesise high fidelity 3D datasets using a single representative 2D image.
  arXiv  Detail & Related papers  (2021-02-10T18:46:17Z)
• 3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications [7.449993399792031]
  3DMaterialGAN is capable of recognizing and synthesizing individual grains whose morphology conforms to a given 3D polycrystalline material microstructure. We show that this method performs comparably or better than state-of-the-art on benchmark annotated 3D datasets. This framework lays the foundation for the recognition and synthesis of polycrystalline material microstructures.
  arXiv  Detail & Related papers  (2020-07-27T21:55:16Z)
• Towards Realistic 3D Embedding via View Alignment [53.89445873577063]
  This paper presents an innovative View Alignment GAN (VA-GAN) that composes new images by embedding 3D models into 2D background images realistically and automatically. VA-GAN consists of a texture generator and a differential discriminator that are inter-connected and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-07-14T14:45:00Z)
• Learning Local Neighboring Structure for Robust 3D Shape Representation [143.15904669246697]
  Representation learning for 3D meshes is important in many computer vision and graphics applications. We propose a local structure-aware anisotropic convolutional operation (LSA-Conv) Our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-21T13:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.