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Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision.
Adam: A method for stochastic optimization
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Shapenet: An information-rich 3D model repository
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Deep generative image models using a laplacian pyramid of adversarial networks
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3D shapenets: A deep representation for volumetric shapes
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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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Volumetric and multi-view cnns for object classification on 3D data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 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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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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A point set generation network for 3D object reconstruction from a single image
H. Fan, H. Su, and L. Guibas · 2017
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J. Gwak, C. B. Choy, M. Chandraker, A. Garg, and S. Savarese · 2017
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Hierarchical surface prediction for 3D object reconstruction
C. Häne, S. Tulsiani, and J. Malik · 2017
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Learning a multi-view stereo machine
A. Kar, C. Häne, and J. Malik · 2017
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Pointnet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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Octnetfusion: Learning depth fusion from data
G. Riegler, A. O. Ulusoy, H. Bischof, and A. Geiger · 2017
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Octnet: Learning deep 3D representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
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AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
T. Groueix, M. Fisher, V. G. Kim, B. Russell, and M. Aubry · 2018
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Fast and accurate image super-resolution with deep laplacian pyramid networks
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2018
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Y. Li, R. Bu, M. Sun, and B. Chen · 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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3D-LMNet: Latent embedding matching for accurate and diverse 3D point cloud reconstruction from a single image
P. Mandikal, N. K L, M. Agarwal, and R. V. Babu · 2018
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Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik · 2017
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O-cnn: Octree-based convolutional neural networks for 3D shape analysis
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong · 2017
Cited alongside, same era.
Marrnet: 3D shape reconstruction via 2.5 d sketches
J. Wu, Y. Wang, T. Xue, X. Sun, B. Freeman, and J. Tenenbaum · 2017
Cited alongside, same era.
Rethinking reprojection: Closing the loop for pose-aware shape reconstruction from a single image
R. Zhu, H. K. Galoogahi, C. Wang, and S. Lucey · 2017
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Representation learning and adversarial generation of 3D point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas · 2018
Cited alongside, same era.
P. Mandikal, N. K L, and R. V. Babu · 2018
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Splatnet: Sparse lattice networks for point cloud processing
H. Su, V. Jampani, D. Sun, S. Maji, V. Kalogerakis, M.-H. Yang, and J. Kautz · 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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Pu-net: Point cloud upsampling network
L. Yu, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng · 2018
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Pcn: Point completion network
W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert · 2018
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CAPNet: Continuous approximation projection for 3D point cloud reconstruction using 2D supervision
N. K L, P. Mandikal, M. Agarwal, and R. V. Babu · 2019
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