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One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks.
Free-form deformation of solid geometric models
T. Sederberg and S. Parry · 1986
Earlier work this paper cites.
Robust estimation of 3D human poses from a single image
C. Wang, Y. Wang, Z. Lin, A. L. Yuille, and W. Gao · 2014
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Beyond PASCAL: A benchmark for 3D object detection in the wild
Y. Xiang, R. Mottaghi, and S. Savarese · 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
Earlier work this paper cites.
Towards probabilistic volumetric reconstruction using ray potential
A. O. Ulusoy, A. Geiger, and M. J. Black · 2015
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
3D shape estimation from 2D landmarks: A convex relaxation approach
X. Zhou, S. Leonardos, X. Hu, and K. Daniilidis · 2015
Earlier work this paper cites.
Marr Revisited: 2D-3D model alignment via surface normal prediction
A. Bansal, B. Russell, and A. Gupta · 2016
Earlier work this paper cites.
Multi-label semantic 3D reconstruction using voxel blocks
I. Cherabier, C. Häne, M. R. Oswald, and M. Pollefeys · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning a predictable and generative vector representation for objects
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta · 2016
Earlier work this paper cites.
Single view 3D reconstruction under an uncalibrated camera and an unknown mirror sphere
K. Han, K. Y. K. Wong, and X. Tan · 2016
Earlier work this paper cites.
Unsupervised learning of 3D structure from images
D. J. Rezende, S. M. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
Earlier work this paper cites.
Structure from category: A generic and prior-less approach
C. Kong, R. Zhu, H. Kiani, and S. Lucey · 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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VConv-DAE: Deep volumetric shape learning without object labels
A. Sharma, O. Grau, and M. Fritz · 2016
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Single image 3D interpreter network
J. Wu, T. Xue, J. J. Lim, Y. Tian, J. B. Tenenbaum, A. Torralba, and W. T. Freeman · 2016
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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
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Perspective transformer nets: Learning single-view 3D object reconstruction without 3D supervision
3D shape reconstruction from sketches via multi-view convolutional networks
Z. Lun, M. Gadelha, E. Kalogerakis, S. Maji, and R. Wang · 2017
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PolyFit: Polygonal surface reconstruction from point clouds
L. Nan and P. Wonka · 2017
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Compact model representation for 3D reconstruction
J. K. Pontes, C. Kong, A. Eriksson, C. Fookes, S. Sridharan, and S. Lucey · 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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OctNet: Learning deep 3D representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
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X. Yan, J. Yang, E. Yumer, Y. Guo, and H. Lee · 2016
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Sparseness meets deepness: 3D human pose estimation from monocular video
X. Zhou, M. Zhu, S. Leonardos, K. G. Derpanis, and K. Daniilidis · 2016
Cited alongside, same era.
A point set generation network for 3D object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Weakly supervised generative adversarial networks for 3D reconstruction
J. Gwak, C. B. Choy, A. Garg, M. Chandraker, and S. Savarese · 2017
Cited alongside, same era.
Hierarchical surface prediction for 3D object reconstruction
C. Häne, S. Tulsiani, and J. Malik · 2017
Cited alongside, same era.
Learning a multi-view stereo machine
A. Kar, C. Häne, and J. Malik · 2017
Cited alongside, same era.
Using locally corresponding CAD models for dense 3D reconstructions from a single image
C. Kong, C.-H. Lin, and S. Lucey · 2017
Cited alongside, same era.
SurfNet: Generating 3D shape surfaces using deep residual network
A. Sinha, A. Unmesh, Q. Huang, and K. Ramani · 2017
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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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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
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MarrNet: 3D Shape Reconstruction via 2.5D Sketches
J. Wu, Y. Wang, T. Xue, X. Sun, W. T. Freeman, and J. B. Tenenbaum · 2017
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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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Learning efficient point cloud generation for dense 3D object reconstruction
C.-H. Lin, C. Kong, and S. Lucey · 2018
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