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In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.
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2017
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2017
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2017
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2015
Cited alongside, same era.
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2015
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2015
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K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ICLR , 2015
2015
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A. Sharma, O. Grau, and M. Fritz, “VConv-DAE : Deep Volumetric Shape Learning Without Object Labels,” ECCV , 2016
2016
Cited alongside, same era.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets,” NIPS , 2016
2016
Cited alongside, same era.
E. Grant, P. Kohli, and M. V. Gerven, “Deep Disentangled Representations for Volumetric Reconstruction,” ECCV Workshops , 2016
2016
Cited alongside, same era.
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta, “Learning a Predictable and Generative Vector Representation for Objects,” ECCV , 2016
2016
Cited alongside, same era.
Later among the works it cites.
P. Dou, S. K. Shah, and I. A. Kakadiaris, “End-to-end 3D face reconstruction with deep neural networks,” CVPR , 2017
2017
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J. Gwak, C. B. Choy, M. Chandraker, A. Garg, and S. Savarese, “Weakly supervised 3D Reconstruction with Adversarial Constraint,” 3DV , 2017
2017
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S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik, “Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency,” CVPR , 2017
2017
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C. Kong, C.-H. Lin, and S. Lucey, “Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image,” CVPR , 2017
2017
Later among the works it cites.
A. Kurenkov, J. Ji, A. Garg, V. Mehta, J. Gwak, C. Choy, and S. Savarese, “DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image,” NIPS , 2017
2017
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J. K. Murthy, G. V. S. Krishna, F. Chhaya, and K. M. Krishna, “Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding,” ICRA , 2017
2017
Later among the works it cites.
A. Johnston, R. Garg, G. Carneiro, I. Reid, and A. v. d. Hengel, “Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image,” ICCV Workshops , 2017
2017
Later among the works it cites.
J. Wu, Y. Wang, T. Xue, X. Sun, W. T. Freeman, and J. B. Tenenbaum, “MarrNet: 3D Shape Reconstruction via 2.5D Sketches,” NIPS , 2017
2017
Later among the works it cites.
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser, “Semantic Scene Completion from a Single Depth Image,” CVPR , 2017
2017
Later among the works it cites.
W. Wang, Q. Huang, S. You, C. Yang, and U. Neumann, “Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks,” ICCV , 2017
2017
Later among the works it cites.
C. Zou, E. Yumer, J. Yang, D. Ceylan, and D. Hoiem, “3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks,” ICCV , 2017
2017
Later among the works it cites.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” CVPR , 2017
2017
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M. Gadelha, S. Maji, and R. Wang, “3D Shape Induction from 2D Views of Multiple Objects,” 3DV , 2017
2017
Later among the works it cites.
E. Smith and D. Meger, “Improved Adversarial Systems for 3D Object Generation and Reconstruction,” CoRL , 2017
2017
Later among the works it cites.
A. A. Soltani, H. Huang, J. Wu, T. D. Kulkarni, and J. B. Tenenbaum, “Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks,” CVPR , 2017
2017
Later among the works it cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” ICML , 2017
2017
Later among the works it cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved Training of Wasserstein GANs,” NIPS , 2017
2017
Later among the works it cites.
M. Arjovsky and L. Bottou, “Towards Principled Methods for Training Generative Adversarial Networks,” ICLR , 2017
2017
Later among the works it cites.
J. Bao, D. Chen, F. Wen, H. Li, and G. Hua, “CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training,” ICCV , 2017
2017
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Y. Mroueh, T. Sercu, and V. Goel, “McGAN: Mean and Covariance Feature Matching GAN,” ICML , 2017
2017
Later among the works it cites.
K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” ICCV , 2017
2017
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Z. Wang, S. Rosa, L. Xie, B. Yang, S. Wang, N. Trigoni, and A. Markham, “Defo-Net: Learning Body Deformation Using Generative Adversarial Networks,” ICRA , 2018
2018
Closest in time.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” ICLR , 2018
2018
Closest in time.
C.-H. Lin, C. Kong, and S. Lucey, “Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction,” AAAI , 2018
2018
Closest in time.