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As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds becomes crucial.
On visual similarity based 3d model retrieval
Ding-Yun Chen, Xiao-Pei Tian, Edward Yu-Te Shen, and Ming Ouhyoung · 2003
Earlier work this paper cites.
Rotation invariant spherical harmonic representation of 3d shape descriptors
Michael M. Kazhdan, Thomas A. Funkhouser, and Szymon Rusinkiewicz · 2003
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Reconstructing street-scenes in real-time from a driving car
Vladyslav Usenko, Jakob Engel, Jörg Stückler, and Daniel Cremers · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Learning a predictable and generative vector representation for objects
Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, and Abhinav Gupta · 2016
Earlier work this paper cites.
Improving variational inference with inverse autoregressive flow
Diederik P. Kingma, Tim Salimans, and Max Welling · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Vconv-dae: Deep volumetric shape learning without object labels
Abhishek Sharma, Oliver Grau, and Mario Fritz · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Comparison of maximum likelihood and gan-based training of real nvps
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, and Peter Dayan · 2017
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Towards a neural statistician
Harrison A Edwards and Amos J. Storkey · 2017
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A point set generation network for 3d object reconstruction from a single image
Gal: Geometric adversarial loss for single-view 3d-object reconstruction
Li Jiang, Shaoshuai Shi, Xiaojuan Qi, and Jiaya Jia · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
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Deformnet: Free-form deformation network for 3d shape reconstruction from a single image
Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher B. Choy, and Silvio Savarese · 2018
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Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, and Ruslan Salakhutdinov · 2018
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Efficient dense point cloud object reconstruction using deformation vector fields
Kejie Li, Trung Pham, Huangying Zhan, and Ian D. Reid · 2018
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Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Cited alongside, same era.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Ryan Prenger, Rafael Valle, and Bryan Catanzaro · 2018
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Pointgrow: Autoregressively learned point cloud generation with self-attention
Yongbin Sun, Yue Wang, Ziwei Liu, Joshua E Siegel, and Sanjay E Sarma · 2018
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Sylvester normalizing flows for variational inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, and Max Welling · 2018
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An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, and Kilian Weinberger · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Patch-based progressive 3d point set upsampling
Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, and Olga Sorkine-Hornung · 2018
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Ec-net: an edge-aware point set consolidation network
Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng · 2018
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Pu-net: Point cloud upsampling network
Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng · 2018
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Adversarial autoencoders for generating 3d point clouds
Maciej Zamorski, Maciej Zieba, Rafał Nowak, Wojciech Stokowiec, and Tomasz Trzciński · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Videoflow: A flow-based generative model for video
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma · 2019
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Pointwise: An unsupervised point-wise feature learning network
Matan Shoef, Sharon Fogel, and Daniel Cohen-Or · 2019
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