Fetching the paper…
Reading the bibliography…
For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
3-d depth reconstruction from a single still image
A. Saxena, S. H. Chung, and A. Y. Ng · 2008
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Opendr: An approximate differentiable renderer
M. M. Loper and M. J. Black · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Earlier work this paper cites.
Fundamentals of computer graphics
S. Marschner and P. Shirley · 2015
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
Earlier work this paper cites.
Inceptionism: Going deeper into neural networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Cited alongside, same era.
Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
J. Yang, S. E. Reed, M.-H. Yang, and H. Lee · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
A learned representation for artistic style
V. Dumoulin, J. Shlens, M. Kudlur, A. Behboodi, F. Lemic, A. Wolisz, M. Molinaro, C. Hirche, M. Hayashi, E. Bagan, et al · 2017
Closest in time.
A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. Guibas · 2017
Closest in time.
3d shape segmentation with projective convolutional networks
E. Kalogerakis, M. Averkiou, S. Maji, and S. Chaudhuri · 2017
Closest in time.
Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
R. Klokov and V. Lempitsky · 2017
Closest in time.
Grass: Generative recursive autoencoders for shape structures
J. Li, K. Xu, S. Chaudhuri, E. Yumer, H. Zhang, and L. Guibas · 2017
Closest in time.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Cited alongside, same era.
Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
Cited alongside, same era.
Unsupervised learning of 3d structure from images
D. J. Rezende, S. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
Cited alongside, same era.
Multi-view 3d models from single images with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Closest in time.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Closest in time.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoys, and A. Geiger · 2017
Closest in time.
Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
Closest in time.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik · 2017
Closest in time.
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
Closest in time.
Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
L. Yi, H. Su, X. Guo, and L. Guibas · 2017
Closest in time.
3d-prnn: Generating shape primitives with recurrent neural networks
C. Zou, E. Yumer, J. Yang, D. Ceylan, and D. Hoiem · 2017
Closest in time.