3D Model Generation and Reconstruction Using Conditional Generative Adversarial Network
- DOI
- 10.2991/ijcis.d.190617.001How to use a DOI?
- Keywords
- 3D model generation; 3D model reconstruction; Generative adversarial network; Class information
- Abstract
Generative adversarial network (GANs) has significant progress in 3D model generation and reconstruction recently years. GANs can generate 3D models by sampling from uniform noise distribution. But they generate randomly and are often not easy to control. To address this problem, we add the class information to both generator and discriminator and construct a new network named 3D conditional GAN. Moreover, to better guide generator to reconstruct 3D model from a single image in high quality, we propose a new 3D model reconstruction network by integrating a classifier into the traditional system. Experimental results on ModelNet10 dataset show that our method can effectively generate realistic 3D models corresponding to the given class labels. And the qualities of 3D model reconstruction have been improved considerably by using proposed method in IKEA dataset.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Haisheng Li AU - Yanping Zheng AU - Xiaoqun Wu AU - Qiang Cai PY - 2019 DA - 2019/06/24 TI - 3D Model Generation and Reconstruction Using Conditional Generative Adversarial Network JO - International Journal of Computational Intelligence Systems SP - 697 EP - 705 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190617.001 DO - 10.2991/ijcis.d.190617.001 ID - Li2019 ER -