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Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications.
Artificial neural networks
M. I. Jordan · 1990
Earlier work this paper cites.
A method for registration of 3-d shapes
P. J. Besl and N. D. McKay · 1992
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Estimating the jacobian of the singular value decomposition: Theory and applications
T. Papadopoulo and M. I. Lourakis · 2000
Earlier work this paper cites.
Robust registration of 2D and 3D point sets
A. W. Fitzgibbon · 2001
Earlier work this paper cites.
Efficient variants of the ICP algorithm
S. Rusinkiewicz and M. Levoy · 2001
Earlier work this paper cites.
Probabilistic matching for 3d scan registration
D. Hähnel and W. Burgard · 2002
Earlier work this paper cites.
Review of combinatorial optimization - theory and algorithms
I. Ivanov · 2002
Earlier work this paper cites.
A non-local algorithm for image denoising
A. Buades, B. Coll, and J.-M. Morel · 2005
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Earlier work this paper cites.
Generalized-icp
A. Segal, D. Hähnel, and S. Thrun · 2009
Earlier work this paper cites.
Sparse iterative closest point
S. Bouaziz, A. Tagliasacchi, and M. Pauly · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
Earlier work this paper cites.
A review of point cloud registration algorithms for mobile robotics
F. Pomerleau, F. Colas, and R. Siegwart · 2015
Earlier work this paper cites.
Pointer networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
Earlier work this paper cites.
Pointer networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shape modeling
Z. Wu, S. Song, A. Khosla, L. Zhang, X. Tang, and J. Xiao · 2015
Cited alongside, same era.
L. J. Ba, R. Kiros, and G. E. Hinton · 2016
Cited alongside, same era.
Point clouds registration with probabilistic data association
R. Y. S. G. Agamennoni, S. Fontana and D. G. Sorrenti · 2016
Cited alongside, same era.
Collaborative 3d reconstruction using heterogeneous uavs: System and experiments
T. Hinzmann, T. Stastny, G. Conte, P. Doherty, P. Rudol, M. Wzorek, E. Galceran, R. Siegwart, and I. Gilitschenski · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Cited alongside, same era.
Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola · 2017
Later among the works it cites.
Point convolutional neural networks by extension operators
M. Atzmon, H. Maron, and Y. Lipman · 2018
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2018
Later among the works it cites.
Splinecnn: Fast geometric deep learning with continuous b-spline kernels
M. Fey, J. E. Lenssen, F. Weichert, and H. Müller · 2018
Later among the works it cites.
Pointcnn: Convolution on x-transformed points
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
Later among the works it cites.
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Point registration via efficient convex relaxation
H. Maron, N. Dym, I. Kezurer, S. Kovalsky, and Y. Lipman · 2016
Cited alongside, same era.
SE-Sync: A certifiably correct algorithm for synchronization over the Special Euclidean group
D. Rosen, L. Carlone, A. Bandeira, and J. Leonard · 2016
Cited alongside, same era.
A short survey of recent advances in graph matching
J. Yan, X.-C. Yin, W. Lin, C. Deng, H. Zha, and X. Yang · 2016
Cited alongside, same era.
Go-icp: A globally optimal solution to 3d icp point-set registration
J. Yang, H. Li, D. Campbell, and Y. Jia · 2016
Cited alongside, same era.
Fast global registration
Q. Zhou, J. Park, and V. Koltun · 2016
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Cited alongside, same era.
Relation networks for object detection
H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei · 2017
Cited alongside, same era.
J. Solomon · 2018
Later among the works it cites.
SPLATNet: Sparse lattice networks for point cloud processing
H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis, M.-H. Yang, and J. Kautz · 2018
Later among the works it cites.
Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
Later among the works it cites.
Graph Attention Networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
Later among the works it cites.
Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
Later among the works it cites.
Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, S. E. S. Ziwei Liu, M. M. Bronstein, and J. M. Solomon · 2018
Later among the works it cites.
Feature denoising for improving adversarial robustness
C. Xie, Y. Wu, L. van der Maaten, A. L. Yuille, and K. He · 2018
Later among the works it cites.
Relational deep reinforcement learning
V. F. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. P. Reichert, T. P. Lillicrap, E. Lockhart, M. Shanahan, V. Langston, R. Pascanu, M. Botvinick, O. Vinyals, and P. Battaglia · 2018
Later among the works it cites.
Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, and M. Sun · 2018
Later among the works it cites.
Open3D: A modern library for 3D data processing
Q.-Y. Zhou, J. Park, and V. Koltun · 2018
Later among the works it cites.
Pointnetlk: Robust & efficient point cloud registration using pointnet
R. A. S. Hunter Goforth, Yasuhiro Aoki and S. Lucey · 2019
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Computational optimal transport
G. Peyré and M. Cuturi · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
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