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Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis.
Rgb-d multi-view system calibration for full 3d scene reconstruction
H. Afzal, D. Aouada, D. Font, B. Mirbach, and B. Ottersten · 2014
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.
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.
Point cloud labeling using 3d convolutional neural network
J. Huang and S. You · 2016
Earlier work this paper cites.
PointNet: Deep learning on point sets for 3d classification and segmentation
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas · 2017
Earlier work this paper cites.
3dcnn-DQN-RNN: A deep reinforcement learning framework for semantic parsing of large-scale 3d point clouds
F. Liu, S. Li, L. Zhang, C. Zhou, R. Ye, Y. Wang, and J. Lu · 2017
Cited alongside, same era.
3d shape reconstruction from sketches via multi-view convolutional networks
Z. Lun, M. Gadelha, E. Kalogerakis, S. Maji, and R. Wang · 2017
Cited alongside, same era.
SEGCloud: Semantic segmentation of 3d point clouds
L. Tchapmi, C. Choy, I. Armeni, J. Gwak, and S. Savarese · 2017
Cited alongside, same era.
3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
A. Dai and M. Nießner · 2018
Cited alongside, same era.
Recurrent slice networks for 3d segmentation of point clouds
Q. Huang, W. Wang, and U. Neumann · 2018
Cited alongside, same era.
Learning shape correspondence with anisotropic convolutional neural networks
D. Boscaini, J. Masci, E. Rodolà, and M. Bronstein
Cited in the paper.
Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio
Cited in the paper.
Clustering large, multi-level data sets: an approach based on kohonen self organizing maps
A. Ciampi and Y. Lechevallier
Cited in the paper.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nies sner
Cited in the paper.
Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture
C. Hazirbas, L. Ma, C. Domokos, and D. Cremers
Cited in the paper.
Long short-term memory
S. Hochreiter and J. Schmidhuber
Cited in the paper.
Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes
J. Huang, H. Zhang, L. Yi, T. Funkhouser, M. Nießner, and L. J. Guibas
Cited in the paper.
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.
Tangent convolutions for dense prediction in 3d
M. Tatarchenko, J. Park, V. Koltun, and Q.-Y. Zhou · 2018
Later among the works it cites.
R-covnet: Recurrent neural convolution network for 3d object recognition
D. Tchuinkou and C. Bobda · 2018
Later among the works it cites.
3d recurrent neural networks with context fusion for point cloud semantic segmentation
X. Ye, J. Li, H. Huang, L. Du, and X. Zhang · 2018
Later among the works it cites.
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