Cluster Computing

, Volume 22, Supplement 6, pp 15471–15481 | Cite as

Masked 3D conditional generative adversarial network for rock mesh generation

  • Ping Kuang
  • Dingli Luo
  • Haoshuang WangEmail author


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.


GAN Computer vision Machine learning 3D rock model generation 



This work is supported by Sichuan Sci-Tech Support Plan, item number: 2017GZ0025, 2017GZ0321, 2016GZ0313.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChengduChina

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