Fetching the paper…
Reading the bibliography…
Point cloud registration is a fundamental problem in 3D computer vision.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
Earlier work this paper cites.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
Earlier work this paper cites.
An iterative image registration technique with an application to stereo vision
Bruce D Lucas, Takeo Kanade, et al · 1981
Earlier work this paper cites.
A method for registration of 3-d shapes
P. J. Besl and N. D. McKay · 1992
Earlier work this paper cites.
Using spin images for efficient object recognition in cluttered 3d scenes
Andrew E. Johnson and Martial Hebert · 1999
Earlier work this paper cites.
Thrift: Local 3d structure recognition
Alex Flint, Anthony Dick, and Anton Van Den Hengel · 2007
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.
Unique signatures of histograms for local surface description
Federico Tombari, Samuele Salti, and Luigi Di Stefano · 2010
Earlier work this paper cites.
Harris 3d: a robust extension of the harris operator for interest point detection on 3d meshes
Ivan Sipiran and Benjamin Bustos · 2011
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.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
A review of point cloud registration algorithms for mobile robotics
François Pomerleau, Francis Colas, and Roland Siegwart · 2015
Earlier work this paper cites.
Go-icp: A globally optimal solution to 3d icp point-set registration
Jiaolong Yang, Hongdong Li, Dylan Campbell, and Yunde Jia · 2015
Earlier work this paper cites.
A comprehensive performance evaluation of 3d local feature descriptors
Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, Min Lu, Jianwei Wan, and Ngai Ming Kwok · 2016
Earlier work this paper cites.
Point registration via efficient convex relaxation
Haggai Maron, Nadav Dym, Itay Kezurer, Shahar Kovalsky, and Yaron Lipman · 2016
Cited alongside, same era.
Fast global registration
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2016
Cited alongside, same era.
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.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
Real-time point cloud alignment for vehicle localization in a high resolution 3d map
Balázs Nagy and Csaba Benedek · 2018
Cited alongside, same era.
Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
Se-sync: A certifiably correct algorithm for synchronization over the special euclidean group
David M Rosen, Luca Carlone, Afonso S Bandeira, and John J Leonard · 2019
Later among the works it cites.
Deep closest point: Learning representations for point cloud registration
Yue Wang and Justin M Solomon · 2019
Later among the works it cites.
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
Later among the works it cites.
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Open3D: A modern library for 3D data processing
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 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.
4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
Cited alongside, same era.
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
Cited alongside, same era.
Usip: Unsupervised stable interest point detection from 3d point clouds
Jiaxin Li and Gim Hee Lee · 2019
Cited alongside, same era.
Deep global registration
Christopher Choy, Wei Dong, and Vladlen Koltun · 2020
Later among the works it cites.
Learning multiview 3d point cloud registration
Zan Gojcic, Caifa Zhou, Jan D Wegner, Leonidas J Guibas, and Tolga Birdal · 2020
Later among the works it cites.
Deep learning for 3d point clouds: A survey
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun · 2020
Later among the works it cites.
Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences
Xiaoshui Huang, Guofeng Mei, and Jian Zhang · 2020
Later among the works it cites.
Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration
Jiahao Li, Changhao Zhang, Ziyao Xu, Hangning Zhou, and Chi Zhang · 2020
Later among the works it cites.
Rskdd-net: Random sample-based keypoint detector and descriptor
Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, and Alois Knoll · 2020
Later among the works it cites.
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
Later among the works it cites.
Deepgmr: Learning latent gaussian mixture models for registration
Wentao Yuan, Benjamin Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, and Jan Kautz · 2020
Later among the works it cites.
Da4ad: End-to-end deep attention-based visual localization for autonomous driving
Yao Zhou, Guowei Wan, Shenhua Hou, Li Yu, Gang Wang, Xiaofei Rui, and Shiyu Song · 2020
Later among the works it cites.