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We address the generalization ability of recent learning-based point cloud registration methods.
An iterative image registration technique with an application to stereo vision
Bruce D Lucas, Takeo Kanade, et al · 1981
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A method for registration of 3-d shapes
Paul J Besl and Neil D McKay · 1992
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Iterative point matching for registration of free-form curves and surfaces
Zhengyou Zhang · 1994
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Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm
Jean-Yves Bouguet et al · 2001
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The trimmed iterative closest point algorithm
Dmitry Chetverikov, Dmitry Svirko, Dmitry Stepanov, and Pavel Krsek · 2002
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Approximate kD tree search for efficient ICP
Michael Greenspan and Mike Yurick · 2003
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Lucas-Kanade 20 years on: A unifying framework
Simon Baker and Iain Matthews · 2004
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Particle filtering for registration of 2D and 3D point sets with stochastic dynamics
Romeil Sandhu, Samuel Dambreville, and Allen Tannenbaum · 2008
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Fast point feature histograms (FPFH) for 3D registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz · 2009
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Fourier Lucas-Kanade algorithm
Simon Lucey, Rajitha Navarathna, Ahmed Bilal Ashraf, and Sridha Sridharan · 2012
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Scene coordinate regression forests for camera relocalization in RGB-D images
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon · 2013
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SUN3D: A database of big spaces reconstructed using SfM and object labels
Jianxiong Xiao, Andrew Owens, and Antonio Torralba · 2013
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Go-ICP: Solving 3D registration efficiently and globally optimally
Jiaolong Yang, Hongdong Li, and Yunde Jia · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Super 4PCS fast global pointcloud registration via smart indexing
Nicolas Mellado, Dror Aiger, and Niloy J Mitra · 2014
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Extended Lucas-Kanade tracking
Shaul Oron, Aharon Bar-Hille, and Shai Avidan · 2014
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SHOT: Unique signatures of histograms for surface and texture description
Samuele Salti, Federico Tombari, and Luigi Di Stefano · 2014
Cited alongside, same era.
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
Cited alongside, same era.
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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The conditional Lucas & Kanade algorithm
Chen-Hsuan Lin, Rui Zhu, and Simon Lucey · 2016
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Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2016
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Alignnet-3D: Fast point cloud registration of partially observed objects
Johannes Groß, Aljoša Ošep, and Bastian Leibe · 2019
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DeepVCP: An end-to-end deep neural network for point cloud registration
Weixin Lu, Guowei Wan, Yao Zhou, Xiangyu Fu, Pengfei Yuan, and Shiyu Song · 2019
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Taking a deeper look at the inverse compositional algorithm
Zhaoyang Lv, Frank Dellaert, James M Rehg, and Andreas Geiger · 2019
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PCRNet: point cloud registration network using PointNet encoding
Vinit Sarode, Xueqian Li, Hunter Goforth, Yasuhiro Aoki, Rangaprasad Arun Srivatsan, Simon Lucey, and Howie Choset · 2019
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Deep Closest Point: Learning representations for point cloud registration
Yue Wang and Justin M Solomon · 2019
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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
Cited alongside, same era.
3DMatch: Learning local geometric descriptors from RGB-D reconstructions
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser · 2017
Cited alongside, same era.
PPF-FoldNet: Unsupervised learning of rotation invariant 3D local descriptors
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2018
Cited alongside, same era.
PPFNet: Global context aware local features for robust 3D point matching
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2018
Cited alongside, same era.
Deep-LK for efficient adaptive object tracking
Chaoyang Wang, Hamed Kiani Galoogahi, Chen-Hsuan Lin, and Simon Lucey · 2018
Cited alongside, same era.
3DFeat-net: Weakly supervised local 3d features for point cloud registration
Zi Jian Yew and Gim Hee Lee · 2018
Cited alongside, same era.
PointNetLK: Robust & efficient point cloud registration using PointNet
Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, and Simon Lucey · 2019
Cited alongside, same era.
PRNet: Self-supervised learning for partial-to-partial registration
Yue Wang and Justin M. Solomon · 2019
Later among the works it cites.
D3Feat: Joint learning of dense detection and description of 3d local features
Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, and Chiew-Lan Tai · 2020
Closest in time.
Deep global registration
Christopher Choy, Wei Dong, and Vladlen Koltun · 2020
Closest in time.
Learning 3d-3d correspondences for one-shot partial-to-partial registration
Zheng Dang, Fei Wang, and Mathieu Salzmann · 2020
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Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences
Xiaoshui Huang, Guofeng Mei, and Jian Zhang · 2020
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Registration loss learning for deep probabilistic point set registration
Felix Järemo Lawin and Per-Erik Forssén · 2020
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Unsupervised partial point set registration via joint shape completion and registration
Xiang Li, Lingjing Wang, and Yi Fang · 2020
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TEASER: Fast and certifiable point cloud registration
Heng Yang, Jingnan Shi, and Luca Carlone · 2020
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RPM-Net: Robust point matching using learned features
Zi Jian Yew and Gim Hee Lee · 2020
Closest in time.
DeepGMR: Learning latent Gaussian mixture models for registration
Wentao Yuan, Ben Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, and Jan Kautz · 2020
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