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Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data.
Shellnet: Efficient point cloud convolutional neural networks using concentric shells statistics
Z. Zhang, B.-S. Hua, and S.-K. Yeung · 1908
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q.-X. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
Geodesic convolutional neural networks on riemannian manifolds
J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst · 2015
Earlier work this paper cites.
VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition
D. Maturana and S. Scherer · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
3d semantic parsing of large-scale indoor spaces
I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese · 2016
Earlier work this paper cites.
Scenenn: A scene meshes dataset with annotations
B.-S. Hua, Q.-H. Pham, D. T. Nguyen, M.-K. Tran, L.-F. Yu, and S.-K. Yeung · 2016
Earlier work this paper cites.
Fpnn: Field probing neural networks for 3d data
Y. Li, S. Pirk, H. Su, C. R. Qi, and L. J. Guibas · 2016
Earlier work this paper cites.
Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 2016
Earlier work this paper cites.
A scalable active framework for region annotation in 3d shape collections
L. Yi, V. G. Kim, D. Ceylan, I.-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. Guibas · 2016
Earlier work this paper cites.
Geometric deep learning: Going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Niessner · 2017
Earlier work this paper cites.
Learning compact geometric features
M. Khoury, Q.-Y. Zhou, and V. Koltun · 2017
Cited alongside, same era.
Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
R. Klokov and V. Lempitsky · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
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.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
Cited alongside, same era.
O-cnn: Octree-based convolutional neural networks for 3d shape analysis
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong · 2017
Large-scale point cloud semantic segmentation with superpoint graphs
L. Landrieu and M. Simonovsky · 2018
Later among the works it cites.
Pointgrid: A deep network for 3d shape understanding
T. Le and Y. Duan · 2018
Later among the works it cites.
So-net: Self-organizing network for point cloud analysis
J. Li, B. M. Chen, and G. H. Lee · 2018
Later among the works it cites.
Pointcnn: Convolution on x-transformed points
Y. Li, R. Bu, M. Sun, and B. Chen · 2018
Later among the works it cites.
Mining point cloud local structures by kernel correlation and graph pooling
Y. Shen, C. Feng, Y. Yang, and D. Tian · 2018
Later among the works it cites.
Tangent convolutions for dense prediction in 3d
M. Tatarchenko, J. Park, V. Koltun, and Q.-Y. Zhou · 2018
Later among the works it cites.
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Cited alongside, same era.
Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
L. Yi, H. Su, X. Guo, and L. J. Guibas · 2017
Cited alongside, same era.
3DMatch: Learning local geometric descriptors from RGB-D reconstructions
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser · 2017
Cited alongside, same era.
3dmfv: Three-dimensional point cloud classification in real-time using convolutional neural networks
Y. Ben-Shabat, M. Lindenbaum, and A. Fischer · 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.
Learning so (3) equivariant representations with spherical cnns
C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis · 2018
Cited alongside, same era.
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
T. Groueix, M. Fisher, V. G. Kim, B. Russell, and M. Aubry · 2018
Cited alongside, same era.
Adaptive o-cnn: A patch-based deep representation of 3d shapes
P.-S. Wang, C.-Y. Sun, Y. Liu, and X. Tong · 2018
Later among the works it cites.
Attentional shapecontextnet for point cloud recognition
S. Xie, S. Liu, Z. Chen, and Z. Tu · 2018
Later among the works it cites.
Spidercnn: Deep learning on point sets with parameterized convolutional filters
Y. Xu, T. Fan, M. Xu, L. Zeng, and Y. Qiao · 2018
Later among the works it cites.
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.
Point2sequence: Learning the shape representation of 3d point clouds with an attention-based sequence to sequence network
X. Liu, Z. Han, Y.-S. Liu, and M. Zwicker · 2019
Closest in time.
Spherical fractal convolutional neural networks for point cloud recognition
Y. Rao, J. Lu, and J. Zhou · 2019
Closest in time.
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
Closest in time.
Rotation invariant convolutions for 3d point clouds deep learning
Z. Zhang, B.-S. Hua, D. W. Rosen, and S.-K. Yeung · 2019
Closest in time.