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Cluster Computing

Erschienen in:

30.03.2018

Masked 3D conditional generative adversarial network for rock mesh generation

verfasst von: Ping Kuang, Dingli Luo, Haoshuang Wang

Erschienen in: Cluster Computing | Sonderheft 6/2019

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Abstract

3D-GAN (Wu et al. in: Advances in Neural Information Processing Systems, pp. 82–90, 2016) has been introduced as a novel way to generate 3D models. In this paper, we propose a 3D-Masked-CGAN approach to apply in the generation of irregular 3D mesh geometry such as rocks. While there are many ways to generate 3D objects, the generation of irregular 3D models has its own peculiarity. To make a model realistic is extremely time-consuming and in high cost. In order to control the shape of generated 3D models, we extend 3D-GAN by adding conditional information into both the generator and discriminator. It is shown that that this model can generate 3D rock models with effective control over the shapes of generated models.

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Titel: Masked 3D conditional generative adversarial network for rock mesh generation
verfasst von: Ping Kuang
Dingli Luo
Haoshuang Wang
Publikationsdatum: 30.03.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 6/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2653-6

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