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Point cloud registration has seen recent success with several learning-based methods that focus on correspondence matching and, as such, optimize only for this objective.
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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An iterative image registration technique with an application to stereo vision
Bruce D Lucas, Takeo Kanade, et al · 1981
Earlier work this paper cites.
Least-squares fitting of two 3-d point sets
K Somani Arun, Thomas S Huang, and Steven D Blostein · 1987
Earlier work this paper cites.
Least-squares estimation of transformation parameters between two point patterns
Shinji Umeyama · 1991
Earlier work this paper cites.
Method for registration of 3-d shapes
Paul J Besl and Neil D McKay · 1992
Earlier work this paper cites.
Model-based classification of quadric surfaces
T.S. Newman, P.J. Flynn, and A.K. Jain · 1993
Earlier work this paper cites.
Using spin images for efficient object recognition in cluttered 3d scenes
A.E. Johnson and M. Hebert · 1999
Earlier work this paper cites.
Aligning point cloud views using persistent feature histograms
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton, and Michael Beetz · 2008
Earlier work this paper cites.
Fast point feature histograms (FPFH) for 3D registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz · 2009
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Fixing the locally optimized ransac–full experimental evaluation
Karel Lebeda, Jirı Matas, and Ondrej Chum · 2012
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Dsac-differentiable ransac for camera localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, and Carsten Rother · 2017
Earlier work this paper cites.
In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe · 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.
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.
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
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
Later among the works it cites.
Pcam: Product of cross-attention matrices for rigid registration of point clouds
Anh-Quan Cao, Gilles Puy, Alexandre Boulch, and Renaud Marlet · 2021
Later among the works it cites.
Stickypillars: Robust and efficient feature matching on point clouds using graph neural networks
Kai Fischer, Martin Simon, Florian Olsner, Stefan Milz, Horst-Michael Gross, and Patrick Mader · 2021
Later among the works it cites.
Predator: Registration of 3d point clouds with low overlap
Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler · 2021
Later among the works it cites.
A hierarchical network for large-scale outdoor lidar point cloud registration
F Lu, G Chen, Y Liu, L Zhang, S Qu, S Liu, and R HRegNet Gu · 2021
Later among the works it cites.
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Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, 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.
Learning multiview 3d point cloud registration
Zan Gojcic, Caifa Zhou, Jan D Wegner, Leonidas J Guibas, and Tolga Birdal · 2020
Cited alongside, same era.
3dregnet: A deep neural network for 3d point registration
G Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C Nascimento, Rama Chellappa, and Pedro Miraldo · 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.
Rpm-net: Robust point matching using learned features
Zi Jian Yew and Gim Hee Lee · 2020
Cited alongside, same era.
Omnet: Learning overlapping mask for partial-to-partial point cloud registration
Hao Xu, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, and Bing Zeng · 2021
Later among the works it cites.
Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration
Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, and Slobodan Ilic · 2021
Later among the works it cites.
https://github.com/qinzheng93/GeoTransformer
GeoTransformer Github Page · 2022
Later among the works it cites.
Sc2-pcr: A second order spatial compatibility for efficient and robust point cloud registration
Z. Chen, K. Sun, F. Yang, and W. Tao · 2022
Later among the works it cites.
Geometric transformer for fast and robust point cloud registration
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, and Kai Xu · 2022
Later among the works it cites.
Regtr: End-to-end point cloud correspondences with transformers
Zi Jian Yew and Gim Hee Lee · 2022
Later among the works it cites.
https://github.com/XuyangBai/PointDSC
PointDSC Github Page · 2023
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