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We propose a novel approach for probabilistic generative modeling of 3D shapes.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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On autoencoders and score matching for energy based models
Kevin Swersky, Marc’Aurelio Ranzato, David Buchman, Benjamin M Marlin, and Nando de Freitas · 2011
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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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VoxNet: A 3D convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Generative and discriminative voxel modeling with convolutional neural networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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A large dataset of object scans
Sungjoon Choi, Qian-Yi Zhou, Stephen Miller, and Vladlen Koltun · 2016
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3D-R2N2: A unified approach for single and multi-view 3D object reconstruction
Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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3D U-Net: Learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
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Volumetric and multi-view CNNs for object classification on 3D data supplementary material
Charles R Qi, Hao Su, Matthias Nießner Angela Dai Mengyuan Yan, and Leonidas J Guibas · 2016
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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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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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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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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 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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Multiresolution tree networks for 3D point cloud processing
Matheus Gadelha, Rui Wang, and Subhransu Maji · 2018
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AtlasNet: A Papier-Mâché approach to learning 3D surface generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan Russell, and Mathieu Aubry · 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 Choy, and Silvio Savarese · 2018
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PointGrid: A deep network for 3D shape understanding
Truc Le and Ye Duan · 2018
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PointCNN: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
Learning localized generative models for 3D point clouds via graph convolution
Diego Valsesia, Giulia Fracastoro, and Enrico Magli · 2019
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Dynamic graph CNN for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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PointFlow: 3D point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Learning gradient fields for shape generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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Cited alongside, same era.
Learning descriptor networks for 3D shape synthesis and analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Zhu Song-Chun, and Ying Nian Wu · 2018
Cited alongside, same era.
FoldingNet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Learning to reconstruct shapes from unseen classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B Tenenbaum, William T Freeman, and Jiajun Wu · 2018
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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3D volumetric modeling with introspective neural networks
Wenlong Huang, Brian Lai, Weijian Xu, and Zhuowen Tu · 2019
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Point cloud GAN
Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, and Ruslan Salakhutdinov · 2019
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Discrete point flow networks for efficient point cloud generation
Roman Klokov, Edmond Boyer, and Jakob Verbeek · 2020
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Morphing and sampling network for dense point cloud completion
Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and Shi-Min Hu · 2020
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PV-RCNN: Point-voxel feature set abstraction for 3D object detection
Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2020
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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 · 2020
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VoxSegNet: Volumetric cnns for semantic part segmentation of 3D shapes
Zongji Wang and Feng Lu · 2020
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Multimodal shape completion via conditional generative adversarial networks
Rundi Wu, Xuelin Chen, Yixin Zhuang, and Baoquan Chen · 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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Diffusion probabilistic models for 3D point cloud generation
Shitong Luo and Wei Hu · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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