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Learning an effective representation of 3D point clouds requires a good metric to measure the discrepancy between two 3D point sets, which is non-trivial due to their irregularity.
Fast and robust earth mover’s distance
O. Pele and M. Werman · 2009
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Multi-scale non-rigid point cloud registration using robust sliced-wasserstein distance via laplace-beltrami eigenmap
Rongjie Lai and Hongkai Zhao · 2014
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Sliced and Radon Wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
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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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Robust reconstruction of indoor scenes
Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun · 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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Sliced wasserstein kernels for probability distributions
Soheil Kolouri, Yang Zou, and Gustavo K. Rohde · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 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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Multilevel clustering via Wasserstein means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, and Dinh Phung · 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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3DMatch: Learning local geometric descriptors from RGB-D reconstructions
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser · 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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PPF-FoldNet: Unsupervised learning of rotation invariant 3D local descriptors
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2018
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PPFNet: Global context aware local features for robust 3D point matching
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2018
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Generative modeling using the sliced wasserstein distance
Ishan Deshpande, Ziyu Zhang, and Alexander G. Schwing · 2018
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A papier-mâché approach to learning 3d surface generation
Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry · 2018
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Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
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Sliced wasserstein distance for learning gaussian mixture models
Soheil Kolouri, Gustavo K. Rohde, and Heiko Hoffmann · 2018
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Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, and Ruslan Salakhutdinov · 2018
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Learning efficient point cloud generation for dense 3D object reconstruction
Chen-Hsuan Lin, Chen Kong, and Simon Lucey · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 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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PU-Net: Point cloud upsampling network
Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng · 2018
On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
Tianyi Lin, Nhat Ho, and Michael Jordan · 2019
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On the efficiency of Sinkhorn and Greenkhorn and their acceleration for optimal transport
Tianyi Lin, Nhat Ho, and Michael I. Jordan · 2019
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Asymptotic guarantees for learning generative models with the sliced-wasserstein distance
Kimia Nadjahi, Alain Durmus, Umut Simsekli, and Roland Badeau · 2019
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JSIS3D: Joint semantic-instance segmentation of 3D point clouds with multi-task pointwise networks and multi-value conditional random fields
Quang-Hieu Pham, Duc Thanh Nguyen, Binh-Son Hua, Gemma Roig, and Sai-Kit Yeung · 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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Geometric disentanglement for generative latent shape models
Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, and Sven Dickinson · 2019
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Strong equivalence between metrics of Wasserstein type
Erhan Bayraktar and Gaoyue Guo · 2019
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Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
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3D local features for direct pairwise registration
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2019
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Max-sliced wasserstein distance and its use for gans
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, and Alexander G. Schwing · 2019
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3D point cloud denoising via deep neural network based local surface estimation
Chaojing Duan, Siheng Chen, and Jelena Kovacevic · 2019
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Orthogonal estimation of wasserstein distances
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, and Adrian Weller · 2019
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3D point cloud generative adversarial network based on tree structured graph convolutions
Dong Wook Shu, Sung Woo Park, and Junseok Kwon · 2019
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Sliced wasserstein generative models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, and Luc Van Gool · 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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3D point capsule networks
Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari · 2019
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Progressive point cloud deconvolution generation network
Le Hui, Rui Xu, Jin Xie, Jianjun Qian, and Jian Yang · 2020
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Computational optimal transport, 2020
Gabriel Peyré and Marco Cuturi · 2020
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LCD: Learned cross-domain descriptors for 2D-3D matching
Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen, Gemma Roig, and Sai-Kit Yeung · 2020
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PointGrow: Autoregressively learned point cloud generation with self-attention
Yongbin Sun, Yue Wang, Ziwei Liu, Joshua Siegel, and Sanjay Sarma · 2020
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On efficient multilevel clustering via Wasserstein distances
Viet Huynh, Nhat Ho, Nhan Dam, Long Nguyen, Mikhail Yurochkin, Hung Bui, and Dinh Phung · 2021
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LAMDA: Label matching deep domain adaptation
Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, and Dinh Phung · 2021
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Distributional sliced-Wasserstein and applications to generative modeling
Khai Nguyen, Nhat Ho, Tung Pham, and Hung Bui · 2021
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Improving relational regularized autoencoders with spherical sliced fused Gromov Wasserstein
Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, and Hung Bui · 2021
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