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Most of the existing self-supervised feature learning methods for 3D data either learn 3D features from point cloud data or from multi-view images.
Illumination for computer generated pictures
Bui Tuong Phong · 1975
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On visual similarity based 3d model retrieval
Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung · 2003
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Rotation invariant spherical harmonic representation of 3 d shape descriptors
Michael Kazhdan, Thomas Funkhouser, and Szymon Rusinkiewicz · 2003
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Image classification with the fisher vector: Theory and practice
Jorge Sánchez, Florent Perronnin, Thomas Mensink, and Jakob Verbeek · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 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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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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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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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
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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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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
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Unsupervised learning of visual representions by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 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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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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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2017
Cited alongside, same era.
Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
Roman Klokov and Victor Lempitsky · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2017
Cited alongside, same era.
Learning features by watching objects move
Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, and Bharath Hariharan · 2017
Cited alongside, same era.
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.
Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2019
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Meshnet: mesh neural network for 3d shape representation
Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, and Yue Gao · 2019
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Unsupervised multi-task feature learning on point clouds
Kaveh Hassani and Mike Haley · 2019
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Momentum contrast for unsupervised visual representation learning
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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
Cited alongside, same era.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 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.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
Cited alongside, same era.
Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
Cited alongside, same era.
Self-supervised spatiotemporal feature learning by video geometric transformations
Longlong Jing and Yingli Tian · 2018
Cited alongside, same era.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2019
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2019
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Self-supervised deep learning on point clouds by reconstructing space
Jonathan Sauder and Bjarne Sievers · 2019
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Mortonnet: Self-supervised learning of local features in 3d point clouds
Ali Thabet, Humam Alwassel, and Bernard Ghanem · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 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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Unsupervised feature learning for point cloud understanding by contrasting and clustering using graph convolutional neural networks
Ling Zhang and Zhigang Zhu · 2019
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3d point capsule networks
Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Watching the world go by: Representation learning from unlabeled videos
Daniel Gordon, Kiana Ehsani, Dieter Fox, and Ali Farhadi · 2020
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Self-supervised feature learning by cross-modality and cross-view correspondences
Longlong Jing, Yucheng Chen, Ling Zhang, Mingyi He, and Yingli Tian · 2020
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Multi-modal self-supervision from generalized data transformations
Mandela Patrick, Yuki M Asano, Ruth Fong, João F Henriques, Geoffrey Zweig, and Andrea Vedaldi · 2020
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