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Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE).
Marching cubes: A high resolution 3d surface construction algorithm
W. E. Lorensen and H. E. Cline · 1987
Earlier work this paper cites.
On visual similarity based 3d model retrieval
D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung · 2003
Earlier work this paper cites.
Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 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. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
A new graph-based two-sample test for multivariate and object data
H. Chen and J. H. Friedman · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
Earlier work this paper cites.
Hierarchical surface prediction for 3d object reconstruction
C. Häne, S. Tulsiani, and J. Malik · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
Stabilizing training of generative adversarial networks through regularization
K. Roth, A. Lucchi, S. Nowozin, and T. Hofmann · 2017
Cited alongside, same era.
Surfnet: Generating 3d shape surfaces using deep residual networks
A. Sinha, A. Unmesh, Q. Huang, and K. Ramani · 2017
Connections between support vector machines, wasserstein distance and gradient-penalty gans
A. Jolicoeur-Martineau and I. Mitliagkas · 2019
Later among the works it cites.
Point cloud GAN
C. Li, M. Zaheer, Y. Zhang, B. Póczos, and R. Salakhutdinov · 2019
Later among the works it cites.
A convolutional decoder for point clouds using adaptive instance normalization
I. Lim, M. Ibing, and L. Kobbelt · 2019
Later among the works it cites.
Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
Later among the works it cites.
PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
K. Mo, S. Zhu, A. X. Chang, L. Yi, S. Tripathi, L. J. Guibas, and H. Su · 2019
Later among the works it cites.
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Cited alongside, same era.
Learning representations and generative models for 3d point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. J. Guibas · 2018
Cited alongside, same era.
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
T. Groueix, M. Fisher, V. G. Kim, B. Russell, and M. Aubry · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
Cited alongside, same era.
High-resolution image synthesis and semantic manipulation with conditional gans
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro · 2018
Cited alongside, same era.
Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
Cited alongside, same era.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
Later among the works it cites.
Semantic image synthesis with spatially-adaptive normalization
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu · 2019
Later among the works it cites.
3d point cloud generative adversarial network based on tree structured graph convolutions
D. W. Shu, S. W. Park, and J. Kwon · 2019
Later among the works it cites.
Implicit functions in feature space for 3d shape reconstruction and completion
J. Chibane, T. Alldieck, and G. Pons-Moll · 2020
Later among the works it cites.
Local implicit grid representations for 3d scenes
C. M. Jiang, A. Sud, A. Makadia, J. Huang, M. Nießner, and T. Funkhouser · 2020
Later among the works it cites.
Adversarial generation of continuous implicit shape representations
M. Kleineberg, M. Fey, and F. Weichert · 2020
Later among the works it cites.