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Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of correspondences for pose estimation.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
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Object modeling by registration of multiple range images
Y. Chen and G. Medioni · 1991
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Least-squares estimation of transformation parameters between two point patterns
Shinji Umeyama · 1991
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A method for registration of 3-d shapes
Paul J. Besl and Neil D. McKay · 1992
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A volumetric method for building complex models from range images
Brian Curless and Marc Levoy · 1996
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Lucas-kanade 20 years on: A unifying framework
Simon Baker and Iain Matthews · 2004
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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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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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Intrinsic shape signatures: A shape descriptor for 3d object recognition
Yu Zhong · 2009
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NARF: 3D range image features for object recognition
Bastian Steder, Radu Bogdan Rusu, Kurt Konolige, and Wolfram Burgard · 2010
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Unique shape context for 3d data description
Federico Tombari, Samuele Salti, and Luigi Di Stefano · 2010
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Scene coordinate regression forests for camera relocalization in rgb-d images
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon · 2013
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SUN3D: A database of big spaces reconstructed using sfm and object labels
Jianxiong Xiao, Andrew Owens, and Antonio Torralba · 2013
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Unsupervised feature learning for 3d scene labeling
Kevin Lai, Liefeng Bo, and Dieter Fox · 2014
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Robust reconstruction of indoor scenes
Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun · 2015
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FaceNet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Learning to navigate the energy landscape
Julien Valentin, Angela Dai, Matthias Niessner, Pushmeet Kohli, Philip Torr, Shahram Izadi, and Cem Keskin · 2016
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DSAC - Differentiable RANSAC for camera localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, and Carsten Rother · 2017
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Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface re-integration
Angela Dai, Matthias Nießner, Michael Zollöfer, Shahram Izadi, and Christian Theobalt · 2017
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Fine-to-coarse global registration of rgb-d scans
Maciej Halber and Thomas Funkhouser · 2017
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Learning compact geometric features
Marc Khoury, Qian-Yi Zhou, and Vladlen Koltun · 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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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.
PPFNet: Global context aware local features for robust 3d point matching
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2018
Cited alongside, same era.
Learning multiview 3d point cloud registration
Zan Gojcic, Caifa Zhou, Jan D Wegner, Leonidas J Guibas, and Tolga Birdal · 2020
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Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences
Xiaoshui Huang, Guofeng Mei, and Jian Zhang · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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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
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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
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Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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.
Learning to find good correspondences
Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua · 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.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
Cited alongside, same era.
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
Cited alongside, same era.
The perfect match: 3d point cloud matching with smoothed densities
Zan Gojcic, Caifa Zhou, Jan D Wegner, and Andreas Wieser · 2019
Cited alongside, same era.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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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
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 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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DeepGMR: Learning latent gaussian mixture models for registration
Wentao Yuan, Benjamin Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, and Jan Kautz · 2020
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PCAM: Product of cross-attention matrices for rigid registration of point clouds
Anh-Quan Cao, Gilles Puy, Alexandre Boulch, and Renaud Marlet · 2021
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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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
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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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Deep hough voting for robust global registration
Junha Lee, Seungwook Kim, Minsu Cho, and Jaesik Park · 2021
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Pointnetlk revisited
Xueqian Li, Jhony Kaesemodel Pontes, and Simon Lucey · 2021
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An end-to-end transformer model for 3d object detection
Ishan Misra, Rohit Girdhar, and Armand Joulin · 2021
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OMNet: Learning overlapping mask for partial-to-partial point cloud registration
Hao Xu, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, and Bing Zeng · 2021
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Pointr: Diverse point cloud completion with geometry-aware transformers
Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou · 2021
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