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This paper proposes a correspondence-free method for point cloud rotational registration.
A method for registration of 3-d shapes
P. J. Besl and N. D. McKay · 1939
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Quasi-likelihood functions, generalized linear models, and the gauss—newton method
R. W. Wedderburn · 1974
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Closed-form solution of absolute orientation using orthonormal matrices
B. K. P. Horn, H. M. Hilden, and S. Negahdaripour · 1988
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Object modeling by registration of multiple range images
Y. Chen and G. G. Medioni · 1992
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New algorithms for 2D and 3D point matching: Pose estimation and correspondence
S. Gold, A. Rangarajan, C.-P. Lu, S. Pappu, and E. Mjolsness · 1998
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Engineering applications of noncommutative harmonic analysis: with emphasis on rotation and motion groups
G. S. Chirikjian and A. B. Kyatkin · 2001
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Multi-scale EM-ICP: A fast and robust approach for surface registration
S. Granger and X. Pennec · 2002
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Registration of point cloud data from a geometric optimization perspective
N. J. Mitra, N. Gelfand, H. Pottmann, and L. Guibas · 2004
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An ICP variant using a point-to-line metric
A. Censi · 2008
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Generalized-ICP
A. Segal, D. Haehnel, and S. Thrun · 2009
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Stochastic models, information theory, and Lie groups, volume 2: Analytic methods and modern applications , volume 2
G. S. Chirikjian · 2009
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Scene coordinate regression forests for camera relocalization in rgb-d images
J. Shotton, B. Glocker, C. Zach, S. Izadi, A. Criminisi, and A. Fitzgibbon · 2013
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Learning the irreducible representations of commutative lie groups
T. Cohen and M. Welling · 2014
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3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Lie groups, Lie algebras, and representations: an elementary introduction , volume 222
B. Hall · 2015
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
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T. S. Cohen and M. Welling · 2016
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3dmatch: Learning local geometric descriptors from rgb-d reconstructions
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser · 2017
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Learning 3d shape completion under weak supervision
D. Stutz and A. Geiger · 2018
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Ppfnet: Global context aware local features for robust 3d point matching
H. Deng, T. Birdal, and S. Ilic · 2018
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3dfeat-net: Weakly supervised local 3d features for point cloud registration
Z. J. Yew and G. H. Lee · 2018
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Learning so (3) equivariant representations with spherical cnns
C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis · 2018
Cited alongside, same era.
On the generalization of equivariance and convolution in neural networks to the action of compact groups
Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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DeepGMR: Learning latent Gaussian mixture models for registration
W. Yuan, B. Eckart, K. Kim, V. Jampani, D. Fox, and J. Kautz · 2020
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Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences
X. Huang, G. Mei, and J. Zhang · 2020
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Rpm-net: Robust point matching using learned features
Z. J. Yew and G. H. Lee · 2020
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Deep global registration
C. Choy, W. Dong, and V. Koltun · 2020
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3DRegNet: A deep neural network for 3d point registration
G. D. Pais, S. Ramalingam, V. M. Govindu, J. C. Nascimento, R. Chellappa, and P. Miraldo · 2020
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R. Kondor and S. Trivedi · 2018
Cited alongside, same era.
PointNetLK: Robust & efficient point cloud registration using pointnet
Y. Aoki, H. Goforth, R. A. Srivatsan, and S. Lucey · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
Cited alongside, same era.
Pcrnet: Point cloud registration network using pointnet encoding
V. Sarode, X. Li, H. Goforth, Y. Aoki, R. A. Srivatsan, S. Lucey, and H. Choset · 2019
Cited alongside, same era.
The perfect match: 3d point cloud matching with smoothed densities
Z. Gojcic, C. Zhou, J. D. Wegner, and A. Wieser · 2019
Cited alongside, same era.
Fully convolutional geometric features
C. Choy, J. Park, and V. Koltun · 2019
Cited alongside, same era.
Later among the works it cites.
SampleNet: Differentiable point cloud sampling
I. Lang, A. Manor, and S. Avidan · 2020
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D3Feat: Joint learning of dense detection and description of 3d local features
X. Bai, Z. Luo, L. Zhou, H. Fu, L. Quan, and C.-L. Tai · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
F. B. Fuchs, D. E. Worrall, V. Fischer, and M. Welling · 2020
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Generalizing convolutional neural networks for equivariance to Lie groups on arbitrary continuous data
M. Finzi, S. Stanton, P. Izmailov, and A. G. Wilson · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
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Di-fusion: Online implicit 3d reconstruction with deep priors
J. Huang, S.-S. Huang, H. Song, and S.-M. Hu · 2020
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inerf: Inverting neural radiance fields for pose estimation
L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin · 2020
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Vector neurons: A general framework for SO(3)-equivariant networks
C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. Guibas · 2021
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Spinnet: Learning a general surface descriptor for 3d point cloud registration
S. Ao, Q. Hu, B. Yang, A. Markham, and Y. Guo · 2021
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A new framework for registration of semantic point clouds from stereo and RGB-D cameras
R. Zhang, T.-Y. Lin, C. E. Lin, S. A. Parkison, W. Clark, J. W. Grizzle, R. M. Eustice, and M. Ghaffari · 2021
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