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We study the problem of extracting accurate correspondences for point cloud registration.
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Attention is all you need
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So-net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M Chen, and Gim Hee Lee · 2018
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Pointnetlk: Robust & efficient point cloud registration using pointnet
Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, and Simon Lucey · 2019
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Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
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Prnet: self-supervised learning for partial-to-partial registration
Spinnet: Learning a general surface descriptor for 3d point cloud registration
Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, and Yulan Guo · 2021
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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
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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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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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Robust point cloud registration framework based on deep graph matching
Kexue Fu, Shaolei Liu, Xiaoyuan Luo, and Manning Wang · 2021
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Yue Wang and Justin Solomon · 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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Modeling point clouds with self-attention and gumbel subset sampling
Jiancheng Yang, Qiang Zhang, Bingbing Ni, Linguo Li, Jinxian Liu, Mengdie Zhou, and Qi Tian · 2019
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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
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Deep global registration
Christopher Choy, Wei Dong, and Vladlen Koltun · 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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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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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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Hregnet: A hierarchical network for large-scale outdoor lidar point cloud registration
Fan Lu, Guang Chen, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, and Rongqi Gu · 2021
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Loftr: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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You only hypothesize once: Point cloud registration with rotation-equivariant descriptors
Haiping Wang, Yuan Liu, Zhen Dong, Wenping Wang, and Bisheng Yang · 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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Cofinet: Reliable coarse-to-fine correspondences for robust point cloud registration
Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, and Slobodan Ilic · 2021
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Patch2pix: Epipolar-guided pixel-level correspondences
Qunjie Zhou, Torsten Sattler, and Laura Leal-Taixe · 2021
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