International Journal of Computational Intelligence Systems

Volume 12, Issue 2, 2019, Pages 697 - 705

3D Model Generation and Reconstruction Using Conditional Generative Adversarial Network

Authors
Haisheng Li1, 2, 3, *, Yanping Zheng1, 2, 3, Xiaoqun Wu1, 2, 3, Qiang Cai1, 2, 3
1School of Computer and Information Engineering, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing, 100048, China
2Beijing Key Laboratory of Big Data Technology for Food Safety, No. 33, Fucheng Road, Haidian District, Beijing, 100048, China
3National Engineering Laboratory For Agri-product Quality Traceability, No. 33, Fucheng Road, Haidian District, Beijing, 100048, China
*Corresponding author. Email: lihsh@th.btbu.edu.cn
Corresponding Author
Haisheng Li
Received 5 May 2019, Accepted 8 June 2019, Available Online 24 June 2019.
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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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
697 - 705
Publication Date
2019/06/24
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.190617.001How to use a DOI?
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/).

Cite this article

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  -