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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References
Visualization, I.D.: Inc., speedtree. I, (2010)
Lim, J.J., Pirsiavash, H., Torralba, A.: Parsing IKEA objects: fine pose estimation. In: ICCV (2013)
Smith, E., Meger, D.: Improved adversarial systems for 3d object generation and reconstruction (2017). CoRR http://arxiv.org/abs/1707.09557. 1
Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative adversarial modeling. In: Advances in Neural Information Processing Systems, pp. 82–90 (2016)
Meetshah: meetshah1995/tf-3dgan, Jul 2017. https://github.com/meetshah1995/tf-3dgan (1995)
Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). CoRR abs/1411.1784. http://arxiv.org/abs/1411.1784
Carlson, W.E.: An algorithm and data structure for 3d object synthesis using surface patch intersections. ACM SIGGRAPH Comput. Graph. 16(3), 255–263 (1982)
Buhmann, J.M., Fellner, D.W., Held, M., Ketterer, J., Puzicha, J.: Dithered color quantization. Comput. Graph. Forum 17, 3, C219–C231 (1998). (Proc. Eurographics’98) http://diglib.eg.org/EG/CGF/volume17/issue3/ColQuant98. https://doi.org/10.1111/1467-8659.00269
Chaudhuri, S., Kalogerakis, E., Guibas, L., Koltun, V.: Probabilistic reasoning for assembly-based 3d modeling. In: ACM Transactions on Graphics (TOG), vol. 30, p. 35. ACM (2011)
Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., Dobkin, D.: Modeling by example. In: ACM Transactions on Graphics (TOG), vol. 23, pp. 652–663. ACM (2004)
Kreavoy, V., Julius, D., Sheffer, A.: Model composition from interchangeable components. In: 15th Pacific Conference on Computer Graphics and Applications, 2007. PG’07, pp. 129–138. IEEE (2007)
Merrell, P., Schkufza, E., Koltun, V.: Computergenerated residential building layouts. In: ACM Transactions on Graphics (TOG), vol. 29, p. 181. ACM (2010)
Peytavie, A., Galin, E., Grosjean, J., Mérillou, S.: Procedural generation of rock piles using aperiodic tiling. In: Computer Graphics Forum, vol. 28, pp. 1801–1809. Wiley (2009)
Hnaidi, H., Guérin, E., Akkouche, S., Peytavie, A., Galin, E.: Feature based terrain generation using diffusion equation. In: Computer Graphics Forum (2010), vol. 29, pp. 2179–2186. Wiley
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Smith, E., Meger, D.: Improved adversarial systems for 3d object generation and reconstruction (2017). arXiv preprint arXiv:1707.09557
Yang, B., Wen, H., Wang, S., Clark, R., Markham, A., Trigoni, N.: 3d object reconstruction from a single depth view with adversarial learning (2017). arXiv preprint arXiv:1708.07969
Gulin, A., Digne, J., Galin, Ã., Peytavie, A., Wolf, C., Benes, B., Martinez, B.: Interactive example-based terrain authoring with conditional generative adversarial networks. ACM Trans. Graph. 36(6), 228 (2017). https://doi.org/10.1145/3130800
Flood fill (2017). https://en.wikipedia.org/wiki/Flood_fill
Geiss, R.: Generating complex procedural terrains using the gpu. GPU Gems 3, 7–37 (2007)
Halbritter, S.: Generative adversarial networks
Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). arXiv preprint arXiv:1411.1784
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. In: ACM Siggraph Computer Graphics, vol. 21, pp. 163–169. ACM (1987)
SP4CERAT: sp4cerat/fast-quadric-mesh-simplification. https://github.com/sp4cerat/Fast-Quadric-Mesh-Simplification (2016)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Acknowledgement
This work is supported by Sichuan Sci-Tech Support Plan, item number: 2017GZ0025, 2017GZ0321, 2016GZ0313.
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Kuang, P., Luo, D. & Wang, H. Masked 3D conditional generative adversarial network for rock mesh generation. Cluster Comput 22 (Suppl 6), 15471–15481 (2019). https://doi.org/10.1007/s10586-018-2653-6
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DOI: https://doi.org/10.1007/s10586-018-2653-6