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Recently normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time.
Auto-encoding variational bayes
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 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, et al · 2015
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Adam: A method for stochastic gradient descent
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 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
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Multi-chart generative surface modeling
Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, and Yaron Lipman · 2018
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Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Multiresolution tree networks for 3d point cloud processing
Matheus Gadelha, Rui Wang, and Subhransu Maji · 2018
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A papier-mâché approach to learning 3d surface generation
Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry · 2018
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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Learning localized generative models for 3d point clouds via graph convolution
Diego Valsesia, Giulia Fracastoro, and Enrico Magli · 2018
Relaxing bijectivity constraints with continuously indexed normalising flows
Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet · 2020
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Gradient boosted normalizing flows
Robert Giaquinto and Arindam Banerjee · 2020
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Semi-supervised learning with normalizing flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson · 2020
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Softflow: Probabilistic framework for normalizing flow on manifolds
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, and Nam Soo Kim · 2020
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Chartpointflow for topology-aware 3d point cloud generation
Takumi Kimura, Takashi Matsubara, and Kuniaki Uehara · 2020
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Discrete point flow networks for efficient point cloud generation
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Pixel2mesh: Generating 3d mesh models from single rgb images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 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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Guided image generation with conditional invertible neural networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 2019
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A rad approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, and Hugo Larochelle · 2019
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2019
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3d point cloud generative adversarial network based on tree structured graph convolutions
Dong Wook Shu, Sung Woo Park, and Junseok Kwon · 2019
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Roman Klokov, Edmond Boyer, and Jakob Verbeek · 2020
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Srflow: Learning the super-resolution space with normalizing flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Variational mixture of normalizing flows
Guilherme GP Pires and Mário AT Figueiredo · 2020
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C-flow: Conditional generative flow models for images and 3d point clouds
Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, and Vittorio Ferrari · 2020
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Hyperflow: Representing 3d objects as surfaces
Przemysław Spurek, Maciej Zięba, Jacek Tabor, and Tomasz Trzciński · 2020
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Pointgrow: Autoregressively learned point cloud generation with self-attention
Yongbin Sun, Yue Wang, Ziwei Liu, Joshua Siegel, and Sanjay Sarma · 2020
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Unsupervised point cloud reconstruction for classific feature learning
An Tao · 2020
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Adversarial autoencoders for compact representations of 3d point clouds
Maciej Zamorski, Maciej Zięba, Piotr Klukowski, Rafał Nowak, Karol Kurach, Wojciech Stokowiec, and Tomasz Trzciński · 2020
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Variational transformer networks for layout generation
Diego Martin Arroyo, Janis Postels, and Federico Tombari · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Neural parts: Learning expressive 3d shape abstractions with invertible neural networks
Despoina Paschalidou, Angelos Katharopoulos, Andreas Geiger, and Sanja Fidler · 2021
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Deflow: Learning complex image degradations from unpaired data with conditional flows
Valentin Wolf, Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2021
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