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Point Cloud Registration (PCR) estimates the relative rigid transformation between two point clouds of the same scene.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
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Animating rotation with quaternion curves
Ken Shoemake · 1985
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Method for registration of 3-d shapes
Paul J Besl and Neil D McKay · 1992
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Object modelling by registration of multiple range images
Yang Chen and Gérard Medioni · 1992
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Recognizing objects in range data using regional point descriptors
Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bülow, and Jitendra Malik · 2004
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Scale-dependent/invariant local 3d shape descriptors for fully automatic registration of multiple sets of range images
John Novatnack and Ko Nishino · 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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Generalized-icp
Aleksandr Segal, Dirk Haehnel, and Sebastian Thrun · 2009
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Surface feature detection and description with applications to mesh matching
Andrei Zaharescu, Edmond Boyer, Kiran Varanasi, and Radu Horaud · 2009
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Unique signatures of histograms for local surface description
Federico Tombari, Samuele Salti, and Luigi Di Stefano · 2010
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Rotational projection statistics for 3d local surface description and object recognition
Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, and Jianwei Wan · 2013
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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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Gogma: Globally-optimal gaussian mixture alignment
Dylan Campbell and Lars Petersson · 2016
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Fast global registration
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Risas: A novel rotation, illumination, scale invariant appearance and shape feature
Kanzhi Wu, Xiaoyang Li, Ravindra Ranasinghe, Gamini Dissanayake, and Yong Liu · 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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So-net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M Chen, 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
Spinnet: Learning a general surface descriptor for 3d point cloud registration
Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, and Yulan Guo · 2021
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Pointdsc: Robust point cloud registration using deep spatial consistency
Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, and Chiew-Lan Tai · 2021
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Predator: Registration of 3d point clouds with low overlap
Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler · 2021
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Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration
Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, and Slobodan Ilic · 2021
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Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
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Cited alongside, same era.
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
Cited alongside, same era.
Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
Cited alongside, same era.
Deep closest point: Learning representations for point cloud registration
Yue Wang and Justin M Solomon · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Deep global registration
Christopher Choy, Wei Dong, and Vladlen Koltun · 2020
Cited alongside, same era.
Circle loss: A unified perspective of pair similarity optimization
Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, and Yichen Wei · 2020
Cited alongside, same era.
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, and Kai Xu · 2022
Later among the works it cites.
You only hypothesize once: Point cloud registration with rotation-equivariant descriptors
Haiping Wang, Yuan Liu, Zhen Dong, and Wenping Wang · 2022
Later among the works it cites.
One-inlier is first: Towards efficient position encoding for point cloud registration
Fan Yang, Lin Guo, Zhi Chen, and Wenbing Tao · 2022
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Regtr: End-to-end point cloud correspondences with transformers
Zi Jian Yew and Gim Hee Lee · 2022
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Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration
Sheng Ao, Qingyong Hu, Hanyun Wang, Kai Xu, and Yulan Guo · 2023
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Robust point cloud registration framework based on deep graph matching
Kexue Fu, Jiazheng Luo, Xiaoyuan Luo, Shaolei Liu, Chenxi Zhang, and Manning Wang · 2023
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Geotransformer: Fast and robust point cloud registration with geometric transformer
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Slobodan Ilic, Dewen Hu, and Kai Xu · 2023
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Qianliang Wu, Haobo Jiang, Yaqing Ding, Lei Luo, Jin Xie, and Jian Yang · 2023
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3d registration with maximal cliques
Xiyu Zhang, Jiaqi Yang, Shikun Zhang, and Yanning Zhang · 2023
Closest in time.