Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networks

Journal article


Zirek, S. (2023). Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networks. Applied Soft Computing . 5, p. 200051. https://doi.org/10.1016/j.sasc.2023.200051
AuthorsZirek, S.
Abstract

Three-dimensional solid computational representations of natural heterogeneous materials are challenging to generate due to their high degree of randomness and varying scales of patterns, such as veins and cracks, in different sizes and directions. In this regard, this paper introduces a new architecture to synthesise 3D solid material models by using encoding deep convolutional generative adversarial networks (EDCGANs). DCGANs have been useful in generative tasks in relation to image processing by successfully recreating similar results based on adequate training. While concentrating on natural heterogeneous materials, this paper uses an encoding and a decoding DCGAN combined in a similar way to auto-encoders to convert a given image into marble, based on patches. Additionally, the method creates an input dataset from a single 2D high-resolution exemplar. Further, it translates of 2D data, used as a seed, into 3D data to create material blocks. While the results on the Z-axis do not have size restrictions, the X- and Y-axis are constrained by the given image. Using the method, the paper explores possible ways to present 3D solid textures. The modelling potentials of the developed approach as a design tool is explored to synthesise a 3D solid texture of leaf-like material from an exemplar of a leaf image.

KeywordsMaterial synthesis, DCGAN, Texture synthesis, 3D Solid textures, Natural heterogeneous materials
Year2023
JournalApplied Soft Computing
Journal citation5, p. 200051
PublisherElsevier
ISSN2772-9419
Digital Object Identifier (DOI)https://doi.org/10.1016/j.sasc.2023.200051
Publication dates
Print21 Apr 2023
Publication process dates
Accepted10 Apr 2023
Deposited29 Nov 2023
Publisher's version
License
File Access Level
Open
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License: CC BY 4.0
File access level: Open

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