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3D point cloud analysis has drawn a lot of research attention due to its wide applications.
Support vector networks
C. Cortes and V. Vapnik · 1995
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Visualizing high-dimensional data using t-sne
L.J.P. Van der Maaten and G.E. Hinton · 2008
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, and Thomas Brox · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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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.
3d semantic parsing of large-scale indoor spaces
Iro Armeni, Ozan Sener, Amir Roshan Zamir, Helen Jiang, Ioannis K. Brilakis, Martin Fischer, and Silvio Savarese · 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
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
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A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas J. Guibas · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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Modulating early visual processing by language
Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C. Courville · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, 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 J. Guibas · 2018
Cited alongside, same era.
Multiresolution tree networks for 3d point cloud processing
Matheus Gadelha, Rui Wang, and Subhransu Maji · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
So-net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M. Chen, and Gim Hee Lee · 2018
Cited alongside, same era.
Learning a hierarchical latent-variable model of 3d shapes
Shikun Liu, C. Lee Giles, and Alexander Ororbia · 2018
Cited alongside, same era.
3d point capsule networks
Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari · 2019
Later among the works it cites.
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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Label-efficient learning on point clouds using approximate convex decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik G. Learned-Miller, Rui Wang, and Subhransu Maji · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
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Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
Cited alongside, same era.
View inter-prediction GAN: unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions
Zhizhong Han, Mingyang Shang, Yu-Shen Liu, and Matthias Zwicker · 2019
Cited alongside, same era.
Multi-angle point cloud-vae: Unsupervised feature learning for 3d point clouds from multiple angles by joint self-reconstruction and half-to-half prediction
Zhizhong Han, Xiyang Wang, Yu-Shen Liu, and Matthias Zwicker · 2019
Cited alongside, same era.
Unsupervised multi-task feature learning on point clouds
Kaveh Hassani and Mike Haley · 2019
Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Cited alongside, same era.
Self-supervised learning of point clouds via orientation estimation
Omid Poursaeed, Tianxing Jiang, Han Qiao, Nayun Xu, and Vladimir G. Kim · 2020
Later among the works it cites.
Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds
Yongming Rao, Jiwen Lu, and Jie Zhou · 2020
Later among the works it cites.
Unsupervised deep shape descriptor with point distribution learning
Yi Shi, Mengchen Xu, Shuaihang Yuan, and Yi Fang · 2020
Later among the works it cites.
Multi-path region mining for weakly supervised 3d semantic segmentation on point clouds
Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Tzu-Yi Hung, and Lihua Xie · 2020
Later among the works it cites.
Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas J Guibas, and Or Litany · 2020
Later among the works it cites.
Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels
Xun Xu and Gim Hee Lee · 2020
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Sspc-net: Semi-supervised semantic 3d point cloud segmentation network
Mingmei Cheng, Le Hui, Jin Xie, and Jian Yang · 2021
Closest in time.
Self-supervised learning on 3d point clouds by learning discrete generative models
Benjamin Eckart, Wentao Yuan, Chao Liu, and Jan Kautz · 2021
Closest in time.
Spatio-temporal self-supervised representation learning for 3d point clouds
Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu · 2021
Closest in time.
Guided point contrastive learning for semi-supervised point cloud semantic segmentation
Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, and Jiaya Jia · 2021
Closest in time.
Unsupervised point cloud pre-training via occlusion completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matt J. Kusner · 2021
Closest in time.
Self-supervised pretraining of 3d features on any point-cloud
Zaiwei Zhang, Rohit Girdhar, Armand Joulin, and Ishan Misra · 2021
Closest in time.