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Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding.
The earth mover’s distance as a metric for image retrieval
Y. Rubner, C. Tomasi, and L. J. Guibas · 2000
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 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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Learning a predictable and generative vector representation for objects
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta · 2016
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Unsupervised 3D local feature learning by circle convolutional restricted boltzmann machine
Z. Han, Z. Liu, J. Han, C.-M. Vong, S. Bu, and X. Li · 2016
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VConv-DAE: Deep volumetric shape learning without object labels
A. Sharma, O. Grau, and M. Fritz · 2016
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Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
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The heat method for distance computation
K. Crane, C. Weischedel, and M. Wardetzky · 2017
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Shape completion using 3d-encoder-predictor cnns and shape synthesis
A. Dai, C. R. Qi, and M. Nießner · 2017
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A point set generation network for 3D object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
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Mesh convolutional restricted boltzmann machines for unsupervised learning of features with structure preservation on 3D meshes
Z. Han, Z. Liu, J. Han, C.-M. Vong, S. Bu, and C. Chen · 2017
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BoSCC: Bag of spatial context correlations for spatially enhanced 3D shape representation
Z. Han, Z. Liu, C.-M. Vong, Y.-S. Liu, S. Bu, J. Han, and C. Chen · 2017
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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 · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Learning representations and generative models for 3D point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. J. Guibas · 2018
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Y. Z. B. P. R. S. Chun-Liang Li, Manzil Zaheer · 2018
Cited alongside, same era.
Ppf-foldnet: Unsupervised learning of rotation invariant 3D local descriptors
H. Deng, T. Birdal, and S. Ilic · 2018
Cited alongside, same era.
Multiresolution tree networks for 3d point cloud processing
M. Gadelha, R. Wang, and S. Maji · 2018
Cited alongside, same era.
Deep spatiality: Unsupervised learning of spatially-enhanced global and local 3D features by deep neural network with coupled softmax
Foldingnet: Point cloud auto-encoder via deep grid deformation
Y. Yang, C. Feng, Y. Shen, and D. Tian · 2018
Later among the works it cites.
Pcn: Point completion network
W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert · 2018
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Y. Zhao, T. Birdal, H. Deng, and F. Tombari · 2018
Later among the works it cites.
Parts4feature: Learning 3d global features from generally semantic parts in multiple views
Z. Han, X. Liu, Y.-S. Liu, and M. Zwicker · 2019
Closest in time.
Unsupervised learning of 3D local features from raw voxels based on a novel permutation voxelization strategy
Z. Han, Z. Liu, J. Han, C. Vong, S. Bu, and C. Chen · 2019
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Z. Han, Z. Liu, C. Vong, Y.-S. Liu, S. Bu, J. Han, and C. Chen · 2018
Cited alongside, same era.
So-net: Self-organizing network for point cloud analysis
J. Li, B. M. Chen, and G. H. Lee · 2018
Cited alongside, same era.
Pointcnn: Convolution on x-transformed points
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
Cited alongside, same era.
Learning a hierarchical latent-variable model of 3D shapes
S. Liu, C. L. Giles, and A. G. O. II · 2018
Cited alongside, same era.
Mining point cloud local structures by kernel correlation and graph pooling
Y. Shen, C. Feng, Y. Yang, and D. Tian · 2018
Cited alongside, same era.
Pointgrow: Autoregressively learned point cloud generation with self-attention
Y. Sun, Y. Wang, Z. Liu, J. E. Siegel, and S. E. Sarma · 2018
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 · 2018
Cited alongside, same era.
Z. Han, H. Lu, Z. Liu, C.-M. Vong, Y.-S. Liu, M. Zwicker, J. Han, and C. P. Chen · 2019
Closest in time.
View inter-prediction gan: Unsupervised representation learning for 3D shapes by learning global shape memories to support local view predictions
Z. Han, M. Shang, Y.-S. Liu, and M. Zwicker · 2019
Closest in time.
Seqviews2seqlabels: Learning 3D global features via aggregating sequential views by rnn with attention
Z. Han, M. Shang, Z. Liu, C.-M. Vong, Y.-S. Liu, M. Zwicker, J. Han, and C. P. Chen · 2019
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Y2seq2seq: Cross-modal representation learning for 3D shape and text by joint reconstruction and prediction of view and word sequences
Z. Han, M. Shang, X. Wang, Y.-S. Liu, and M. Zwicker · 2019
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3Dviewgraph: Learning global features for 3d shapes from a graph of unordered views with attention
Z. Han, X. Wang, C.-M. Vong, Y.-S. Liu, M. Zwicker, and C. P. Chen · 2019
Closest in time.
Point2sequence: Learning the shape representation of 3D point clouds with an attention-based sequence to sequence network
X. Liu, Z. Han, Y. Liu, and M. Zwicker · 2019
Closest in time.
Neuralsampler: Euclidean point cloud auto-encoder and sampler
E. Remelli, P. Baque, and P. Fua · 2019
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Pointwise:an unsupervised point-wise feature learning network
M. Shoef, S. Fogel, and D. Cohen-Or · 2019
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Learning localized generative models for 3D point clouds via graph convolution
D. Valsesia, G. Fracastoro, and E. Magli · 2019
Closest in time.