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Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
Infants’ transfer of response between two-dimensional and three-dimensional stimuli
Susan A. Rose · 1977
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Picture perception in infancy
Judy S. DeLoache, Mark S. Strauss, and Jane Maynard · 1979
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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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
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 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
L. 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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Look, listen and learn
Relja Arandjelovic and Andrew Zisserman · 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 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 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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Compositional attention networks for machine reasoning
Drew Arad Hudson and Christopher D. Manning · 2018
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So-net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M. Chen, and Gim Hee Lee · 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
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Audio-visual scene analysis with self-supervised multisensory features
Andrew Owens and Alexei A. Efros · 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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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F. Henriques, Philip Torr, and Andrea Vedaldi · 2019
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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
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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
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Unsupervised multi-task feature learning on point clouds
Kaveh Hassani and Mike Haley · 2019
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Relation-shape convolutional neural network for point cloud analysis
Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan · 2019
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Deep hough voting for 3D object detection in point clouds
Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas · 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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KPConv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Francois Goulette, and Leonidas J. Guibas · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 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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H3DNet: 3D object detection using hybrid geometric primitives
Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang · 2020
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Rich semantics improve few-shot learning
Mohamed Afham, Salman Khan, Muhammad Haris Khan, Muzammal Naseer, and Fahad Shahbaz Khan · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Virtex: Learning visual representations from textual annotations
Karan Desai and Justin Johnson · 2021
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Self-contrastive learning with hard negative sampling for self-supervised point cloud learning
Bi’an Du, Xiang Gao, Wei Hu, and Xin Li · 2021
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Self-supervised learning on 3D point clouds by learning discrete generative models
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Adaptive cross-modal few-shot learning
Chen Xing, Negar Rostamzadeh, Boris Oreshkin, and Pedro O O. Pinheiro · 2019
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DISN: Deep implicit surface network for high-quality single-view 3D reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 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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PointMixup: Augmentation for point clouds
Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang, and Cees G. M. Snoek · 2020
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Bootstrap your own latent - a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko · 2020
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Benjamin Eckart, Wentao Yuan, Chao Liu, and Jan Kautz · 2021
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Self-supervised video representation learning by context and motion decoupling
Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, and Rong Jin · 2021
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Spatio-temporal self-supervised representation learning for 3D point clouds
Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu · 2021
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Mdetr - modulated detection for end-to-end multi-modal understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion · 2021
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Point cloud augmentation with weighted local transformations
Sihyeon Kim, Sanghyeok Lee, Dasol Hwang, Jaewon Lee, Seong Jae Hwang, and Hyunwoo J. Kim · 2021
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Point discriminative learning for unsupervised representation learning on 3D point clouds
Fayao Liu, Guosheng Lin, and Chuan-Sheng Foo · 2021
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Learning from 2D: Contrastive pixel-to-point knowledge transfer for 3D pretraining
Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H. Hsu · 2021
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An end-to-end transformer model for 3D object detection
Ishan Misra, Rohit Girdhar, and Armand Joulin · 2021
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Audio-visual instance discrimination with cross-modal agreement
Pedro Morgado, Nuno Vasconcelos, and Ishan Misra · 2021
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Spatiotemporal contrastive video representation learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Exploring complementary strengths of invariant and equivariant representations for few-shot learning
Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah · 2021
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Point cloud pre-training by mixing and disentangling
Chao Sun, Zhedong Zheng, Xiaohan Wang, Mingliang Xu, and Yi Yang · 2021
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Unsupervised point cloud pre-training via occlusion completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matthew J. Kusner · 2021
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Image2Point: 3D point-cloud understanding with pretrained 2d convnets
Chenfeng Xu, Shijia Yang, Bohan Zhai, Bichen Wu, Xiangyu Yue, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2021
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Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi · 2021
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Progressive seed generation auto-encoder for unsupervised point cloud learning
Juyoung Yang, Pyunghwan Ahn, Doyeon Kim, Haeil Lee, and Junmo Kim · 2021
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Self-supervised pretraining of 3D features on any point-cloud
Zaiwei Zhang, Rohit Girdhar, Armand Joulin, and Ishan Misra · 2021
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip H.S. Torr, and Vladlen Koltun · 2021
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Improving contrastive learning by visualizing feature transformation
Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, and Chang Wen Chen · 2021
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