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3D motion estimation including scene flow and point cloud registration has drawn increasing interest.
A relationship between arbitrary positive matrices and doubly stochastic matrices
Richard Sinkhorn · 1964
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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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Method for registration of 3-d shapes
Paul J Besl and Neil D McKay · 1992
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A method for registration of 3-d shapes
Paul J. Besl and Neil D. McKay · 1992
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Three-dimensional registration using range and intensity information
Guy Godin, Marc Rioux, and Rejean Baribeau · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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New algorithms for 2d and 3d point matching: pose estimation and correspondence
Steven Gold, Anand Rangarajan, Chien-Ping Lu, Suguna Pappu, and Eric Mjolsness · 1998
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Using spin images for efficient object recognition in cluttered 3d scenes
Andrew E Johnson and Martial Hebert · 1999
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Three-dimensional scene flow
Sundar Vedula, Simon Baker, Peter Rander, Robert Collins, and Takeo Kanade · 1999
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A feature registration framework using mixture models
Haili Chui and Anand Rangarajan · 2000
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Efficient variants of the icp algorithm
Szymon Rusinkiewicz and Marc Levoy · 2001
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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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A correlation-based approach to robust point set registration
Yanghai Tsin and Takeo Kanade · 2004
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Laplacian Mesh Processing
Olga Sorkine · 2005
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3D free-form object recognition in range images using local surface patches
Hui Chen and Bir Bhanu · 2007
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A variational method for scene flow estimation from stereo sequences
F. Huguet and F. Devernay · 2007
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Aligning point cloud views using persistent feature histograms
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton, and Michael Beetz · 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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Large displacement optical flow computation withoutwarping
Frank Steinbrücker, Thomas Pock, and Daniel Cremers · 2009
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Unique shape context for 3D data description
Federico Tombari, Samuele Salti, and Luigi Di Stefano · 2010
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Kinecting the dots: Particle based scene flow from depth sensors
S. Hadfield and R. Bowden · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Shot: Unique signatures of histograms for surface and texture description
Samuele Salti, Federico Tombari, and Luigi Di Stefano · 2014
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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.
Joint 3d estimation of vehicles and scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
Cited alongside, same era.
A review of point cloud registration algorithms for mobile robotics
François Pomerleau, Francis Colas, and Roland Siegwart · 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
Cited alongside, same era.
PointNetLK: Robust & efficient point cloud registration using pointnet
Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, and Simon Lucey · 2019
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Pointflownet: Learning representations for rigid motion estimation from point clouds
A. Behl, Despoina Paschalidou, S. Donné, and A. Geiger · 2019
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Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds
Xiuye Gu, Y. Wang, Chongruo Wu, Y. Lee, and Panqu Wang · 2019
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USIP: Unsupervised stable interest point detection from 3d point clouds
Jiaxin Li and Gim Hee Lee · 2019
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An algorithm unrolling approach to deep image deblurring
Y. Li, M. Tofighi, V. Monga, and Y. C. Eldar · 2019
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Flownet3d: Learning scene flow in 3d point clouds
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Go-ICP: A globally optimal solution to 3D ICP point-set registration
Jiaolong Yang, Hongdong Li, Dylan Campbell, and Yunde Jia · 2015
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.
A comprehensive performance evaluation of 3D local feature descriptors
Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, Min Lu, Jianwei Wan, and Ngai Ming Kwok · 2016
Cited alongside, same era.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
Cited alongside, same era.
Fast global registration
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2016
Cited alongside, same era.
A novel binary shape context for 3d local surface description
Zhen Dong, Bisheng Yang, Yuan Liu, Fuxun Liang, Bijun Li, and Yufu Zang · 2017
Cited alongside, same era.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
Xingyu Liu, Charles R Qi, and Leonidas J Guibas · 2019
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DeepICP: An end-to-end deep neural network for 3D point cloud registration
Weixin Lu, Guowei Wan, Yao Zhou, Xiangyu Fu, Pengfei Yuan, and Shiyu Song · 2019
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Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
Vishal Monga, Yuelong Li, and Yonina C Eldar · 2019
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A symmetric objective function for icp
Szymon Rusinkiewicz · 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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Prnet: Self-supervised learning for partial-to-partial registration
Yue Wang and Justin M Solomon · 2019
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Flot: Scene flow on point clouds guided by optimal transport
Gilles Puy, Alexandre Boulch, and Renaud Marlet · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Self-supervised learning of non-rigid residual flow and ego-motion
Ivan Tishchenko, Sandro Lombardi, Martin R Oswald, and Marc Pollefeys · 2020
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Pointpwc-net: Cost volume on point clouds for (self-) supervised scene flow estimation
Wenxuan Wu, Zhi Yuan Wang, Zhuwen Li, Wei Liu, and Li Fuxin · 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
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Robust point cloud registration framework based on deep graph matching
Kexue Fu, Shaolei Liu, Xiaoyuan Luo, and Manning Wang · 2021
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Dro: Deep recurrent optimizer for structure-from-motion
Xiaodong Gu, Weihao Yuan, Zuozhuo Dai, Chengzhou Tang, Siyu Zhu, and Ping Tan · 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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Flowstep3d: Model unrolling for self-supervised scene flow estimation
Yair Kittenplon, Yonina C Eldar, and Dan Raviv · 2021
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