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Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
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Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Tobias Springenberg, and T. Brox · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Deep convolutional inverse graphics network
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Deep visual analogy-making
S. E. Reed, Y. Zhang, Y. Zhang, and H. Lee · 2015
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3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
J. Yang, S. E. Reed, M.-H. Yang, and H. Lee · 2015
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
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A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. Guibas · 2016
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3d shape induction from 2d views of multiple objects
M. Gadelha, S. Maji, and R. Wang · 2016
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Learning a predictable and generative vector representation for objects
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta · 2016
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Attribute2image: Conditional image generation from visual attributes
X. Yan, J. Yang, K. Sohn, and H. Lee · 2016
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Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision
X. Yan, J. Yang, E. Yumer, Y. Guo, and H. Lee · 2016
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Learning semantic deformation flows with 3d convolutional networks
M. E. Yumer and N. J. Mitra · 2016
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View synthesis by appearance flow
T. Zhou, S. Tulsiani, W. Sun, J. Malik, and A. A. Efros · 2016
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Generative visual manipulation on the natural image manifold
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros · 2016
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Hierarchical surface prediction for 3d object reconstruction
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V. Hegde and R. Zadeh · 2016
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2016
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Inverse compositional spatial transformer networks
C.-H. Lin and S. Lucey · 2016
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Unsupervised learning of 3d structure from images
D. J. Rezende, S. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
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Multi-view 3d models from single images with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2016
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Generative image modeling using style and structure adversarial networks
X. Wang and A. Gupta · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
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C. Häne, S. Tulsiani, and J. Malik · 2017
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Using locally corresponding cad models for dense 3d reconstructions from a single image
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Transformation-grounded image generation network for novel 3d view synthesis
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
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