Fetching the paper…
Reading the bibliography…
Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms.
Concerning nonnegative matrices and doubly stochastic matrices
Richard Sinkhorn and Paul Knopp · 1967
Earlier work this paper cites.
Calculation of the wasserstein distance between probability distributions on the line
SS Vallender · 1974
Earlier work this paper cites.
Multidimensional binary search trees used for associative searching
Jon Louis Bentley · 1975
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.
A method for registration of 3-d shapes
Paul J. Besl and Neil D. McKay · 1992
Earlier work this paper cites.
Iterative point matching for registration of free-form curves and surfaces
Zhengyou Zhang · 1994
Earlier work this paper cites.
Wide baseline stereo matching based on local, affinely invariant regions
Tinne Tuytelaars and Luc J Van Gool · 2000
Earlier work this paper cites.
Efficient variants of the icp algorithm
Szymon Rusinkiewicz and Marc Levoy · 2001
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
Earlier work this paper cites.
A correlation-based approach to robust point set registration
Yanghai Tsin and Takeo Kanade · 2004
Earlier work this paper cites.
A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus
Rahul Raguram, Jan-Michael Frahm, and Marc Pollefeys · 2008
Earlier work this paper cites.
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja and David G Lowe · 2009
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.
Scramsac: Improving ransac’s efficiency with a spatial consistency filter
Torsten Sattler, Bastian Leibe, and Leif Kobbelt · 2009
Earlier work this paper cites.
Generalized-icp
Aleksandr Segal, Dirk Haehnel, and Sebastian Thrun · 2009
Earlier work this paper cites.
Efficient sequential correspondence selection by cosegmentation
Jan Cech, Jiri Matas, and Michal Perdoch · 2010
Earlier work this paper cites.
Community structure in time-dependent, multiscale, and multiplex networks
Peter J Mucha, Thomas Richardson, Kevin Macon, Mason A Porter, and Jukka-Pekka Onnela · 2010
Earlier work this paper cites.
Point set registration: Coherent point drift
Andriy Myronenko and Xubo Song · 2010
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.
Fast and accurate scan registration through minimization of the distance between compact 3d ndt representations
Todor Stoyanov, Martin Magnusson, Henrik Andreasson, and Achim J Lilienthal · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Cited alongside, same era.
Growing multiplex networks
Vincenzo Nicosia, Ginestra Bianconi, Vito Latora, and Marc Barthelemy · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Loam: Lidar odometry and mapping in real-time
Ji Zhang and Sanjiv Singh · 2014
Cited alongside, same era.
Rigid scene flow for 3d lidar scans
Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, and Wolfram Burgard · 2016
Cited alongside, same era.
Collar line segments for fast odometry estimation from velodyne point clouds
Martin Velas, Michal Spanel, and Adam Herout · 2016
Cited alongside, same era.
3dfeat-net: Weakly supervised local 3d features for point cloud registration
Zi Jian Yew and Gim Hee Lee · 2018
Later among the works it cites.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
Later among the works it cites.
Pointnetlk: Robust efficient point cloud registration using pointnet
Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, and Simon Lucey · 2019
Later among the works it cites.
D2-net: A trainable cnn for joint detection and description of local features
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler · 2019
Later among the works it cites.
Deeplocalization: Landmark-based self-localization with deep neural networks
Nico Engel, Stefan Hoermann, Markus Horn, Vasileios Belagiannis, and Klaus Dietmayer · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lift: Learned invariant feature transform
Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua · 2016
Cited alongside, same era.
Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence
JiaWang Bian, Wen-Yan Lin, Yasuyuki Matsushita, Sai-Kit Yeung, Tan-Dat Nguyen, and Ming-Ming Cheng · 2017
Cited alongside, same era.
Multi-view 3d object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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.
Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds
Xiuye Gu, Yijie Wang, Chongruo Wu, Yong Jae Lee, and Panqu Wang · 2019
Later among the works it cites.
Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
Later among the works it cites.
Usip: Unsupervised stable interest point detection from 3d point clouds
Jiaxin Li and Gim Hee Lee · 2019
Later among the works it cites.
Lo-net: Deep real-time lidar odometry
Qing Li, Shaoyang Chen, Cheng Wang, Xin Li, Chenglu Wen, Ming Cheng, and Jonathan Li · 2019
Later among the works it cites.
Jiarong Lin and Fu Zhang · 2019
Later among the works it cites.
Flownet3d: Learning scene flow in 3d point clouds
Xingyu Liu, Charles R Qi, and Leonidas J Guibas · 2019
Later among the works it cites.
Meteornet: Deep learning on dynamic 3d point cloud sequences
Xingyu Liu, Mengyuan Yan, and Jeannette Bohg · 2019
Later among the works it cites.
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
Later among the works it cites.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
A. Milioto, I. Vizzo, J. Behley, and C. Stachniss · 2019
Later among the works it cites.
R2d2: Repeatable and reliable detector and descriptor
Jerome Revaud, Philippe Weinzaepfel, César De Souza, Noe Pion, Gabriela Csurka, Yohann Cabon, and Martin Humenberger · 2019
Later among the works it cites.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2019
Later among the works it cites.
Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds
Martin Simon, Karl Amende, Andrea Kraus, Jens Honer, Timo Samann, Hauke Kaulbersch, Stefan Milz, and Horst Michael Gross · 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.
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.
Learning multiview 3d point cloud registration
Zan Gojcic, Caifa Zhou, Jan D Wegner, Leonidas J Guibas, and Tolga Birdal · 2020
Closest in time.