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Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets.
Comparing images using the hausdorff distance
Daniel P Huttenlocher, Gregory A. Klanderman, and William J Rucklidge · 1993
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Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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
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Shape completion using 3d-encoder-predictor cnns and shape synthesis
Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner · 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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High-resolution shape completion using deep neural networks for global structure and local geometry inference
Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, and Yizhou Yu · 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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Improved adversarial systems for 3d object generation and reconstruction
Edward J Smith and David Meger · 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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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
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Pcn: Point completion network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert · 2018
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Unpaired point cloud completion on real scans using adversarial training
Xuelin Chen, Baoquan Chen, and Niloy J Mitra · 2019
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3d local features for direct pairwise registration
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2019
Cited alongside, same era.
Total denoising: Unsupervised learning of 3d point cloud cleaning
Pedro Hermosilla, Tobias Ritschel, and Timo Ropinski · 2019
Cited alongside, same era.
Pu-gan: a point cloud upsampling adversarial network
Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng · 2019
Cited alongside, same era.
Pu-gan: a point cloud upsampling adversarial network
Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng · 2019
Cited alongside, same era.
On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
Tianyi Lin, Nhat Ho, and Michael Jordan · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 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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Dpdist: Comparing point clouds using deep point cloud distance
Dahlia Urbach, Yizhak Ben-Shabat, and Michael Lindenbaum · 2020
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Reconfigurable voxels: A new representation for lidar-based point clouds
Tai Wang, Xinge Zhu, and Dahua Lin · 2020
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Cascaded refinement network for point cloud completion
Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee · 2020
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Point cloud completion by skip-attention network with hierarchical folding
Xin Wen, Tianyang Li, Zhizhong Han, and Yu-Shen Liu · 2020
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
What do single-view 3d reconstruction networks learn?
Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
Cited alongside, same era.
Topnet: Structural point cloud decoder
Lyne P Tchapmi, Vineet Kosaraju, Hamid Rezatofighi, Ian Reid, and Silvio Savarese · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
Cited alongside, same era.
Pf-net: Point fractal network for 3d point cloud completion
Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le · 2020
Cited alongside, same era.
Later among the works it cites.
Multimodal shape completion via conditional generative adversarial networks
Rundi Wu, Xuelin Chen, Yixin Zhuang, and Baoquan Chen · 2020
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Grnet: Gridding residual network for dense point cloud completion
Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun · 2020
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Detail preserved point cloud completion via separated feature aggregation
Wenxiao Zhang, Qingan Yan, and Chunxia Xiao · 2020
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Point-set distances for learning representations of 3d point clouds
Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, and Binh-Son Hua · 2021
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Variational relational point completion network
Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang, Haiyu Zhao, Shuai Yi, and Ziwei Liu · 2021
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Style-based point generator with adversarial rendering for point cloud completion
Chulin Xie, Chuxin Wang, Bo Zhang, Hao Yang, Dong Chen, and Fang Wen · 2021
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Unsupervised 3d shape completion through gan inversion
Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy · 2021
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Cylindrical and asymmetrical 3d convolution networks for lidar segmentation
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin · 2021
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