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We address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images.
Multiple choice learning: Learning to produce multiple structured outputs
A. Guzmán-rivera, D. Batra, and P. Kohli · 2012
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What shape are dolphins? Building 3D morphable models from 2D images
T. J. Cashman and A. W. Fitzgibbon · 2013
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OpenDR: An approximate differentiable renderer
M. M. Loper and M. J. Black · 2014
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Reconstructing PASCAL VOC
S. Vicente, J. Carreira, L. Agapito, and J. Batista · 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, J. Xiao, L. Yi, and F. Yu · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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A versatile scene model with differentiable visibility applied to generative pose estimation
H. Rhodin, N. Robertini, C. Richardt, H.-P. Seidel, and C. Theobalt · 2015
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TensorFlow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, et al · 2016
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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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Unsupervised learning of 3D structure from images
D. Rezende, S. M. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum · 2016
Cited alongside, same era.
Perspective transformer nets: Learning single-view 3D object reconstruction without 3D supervision
X. Yan, J. Yang, E. Yumer, Y. Guo, and H. Lee · 2016
Cited alongside, same era.
Photographic image synthesis with cascaded refinement networks
Q. Chen and V. Koltun · 2017
Cited alongside, same era.
A point set generation network for 3D object reconstruction from a single image
Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
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Neural 3D mesh renderer
H. Kato, Y. Ushiku, and T. Harada · 2018
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Learning efficient point cloud generation for dense 3D object reconstruction
C.-H. Lin, C. Kong, and S. Lucey · 2018
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Pix3d: Dataset and methods for single-image 3D shape modeling
X. Sun, J. Wu, X. Zhang, Z. Zhang, C. Zhang, T. Xue, J. B. Tenenbaum, and W. T. Freeman · 2018
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Multi-view consistency as supervisory signal for learning shape and pose prediction
S. Tulsiani, A. A. Efros, and J. Malik · 2018
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3D interpreter networks for viewer-centered wireframe modeling
J. Wu, T. Xue, J. J. Lim, Y. Tian, J. B. Tenenbaum, A. Torralba, and W. T. Freeman · 2018
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H. Fan, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
GRASS: Generative recursive autoencoders for shape structures
J. Li, K. Xu, S. Chaudhuri, E. Yumer, H. Zhang, and L. Guibas · 2017
Cited alongside, same era.
Synthesizing 3D shapes via modeling multi-view depth maps and silhouettes with deep generative networks
A. A. Soltani, H. Huang, J. Wu, T. D. Kulkarni, and J. B. Tenenbaum · 2017
Cited alongside, same era.
Learning category-specific deformable 3D models for object reconstruction
S. Tulsiani, A. Kar, J. Carreira, and J. Malik
Cited in the paper.
Learning shape abstractions by assembling volumetric primitives
S. Tulsiani, H. Su, L. J. Guibas, A. A. Efros, and J. Malik
Cited in the paper.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik
Cited in the paper.
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FoldingNet: Interpretable unsupervised learning on 3D point clouds
Y. Yang, C. Feng, Y. Shen, and D. Tian · 2018
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