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Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars.
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 1901
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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The earth mover’s distance as a metric for image retrieval
Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas · 2000
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Aligning point cloud views using persistent feature histograms
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton, and Michael Beetz · 2008
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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The wave kernel signature: A quantum mechanical approach to shape analysis
Mathieu Aubry, Ulrich Schlickewei, and Daniel Cremers · 2011
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A survey on shape correspondence
Oliver Van Kaick, Hao Zhang, Ghassan Hamarneh, and Daniel Cohen-Or · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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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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3d object recognition in cluttered scenes with local surface features: a survey
Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, Min Lu, and Jianwei Wan · 2014
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3d shapenets for 2.5d object recognition and next-best-view prediction
Zhirong Wu, Shuran Song, Aditya Khosla, Xiaoou Tang, and Jianxiong Xiao · 2014
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Shapenet: An information-rich 3d model repository
Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 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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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, Leonidas Guibas, et al · 2016
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Representation learning and adversarial generation of 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. Guibas · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles Ruizhongtai 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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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 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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3d semantic parsing of large-scale indoor spaces
Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Volumetric and multi-view cnns for object classification on 3d data
Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
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Deep learning with sets and point clouds
Siamak Ravanbakhsh, Jeff Schneider, and Barnabas Poczos · 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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Large-scale 3d shape retrieval from shapenet core55: Shrec’17 track
Manolis Savva, Fisher Yu, Hao Su, Asako Kanezaki, Takahiko Furuya, Ryutarou Ohbuchi, Zhichao Zhou, Rui Yu, Song Bai, Xiang Bai, et al · 2017
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 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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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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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 · 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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Zhizhong Han, Mingyang Shang, Yuhang Liu, and Matthias Zwicker · 2019
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