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The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks.
Neural machine translation by jointly learning to align and translate
Bahdanau, Dzmitry, K. Cho, and Y. Bengio · 2014
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3d shapenets for 2.5d object recognition and next-best-view prediction
Z. Wu, S. Song, A. Khosla, X. Tang, and J. Xiao · 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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A scalable active framework for region annotation in 3d shape collections
Y. Li, V. G. Kim, D. Ceylan, I. C. Shen, M. Yan, S. Hao, C. Lu, Q. Huang, A. Sheffer, and L. Guibas · 2016
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Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Niebner, A. Dai, M. Yan, and L. J. Guibas · 2016
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Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoys, and A. Geiger · 2016
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Joint 2d-3d-semantic data for indoor scene understanding
I. Armeni, S. Sax, A. R. Zamir, and S. Savarese · 2017
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Exploring spatial context for 3d semantic segmentation of point clouds
F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe · 2017
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Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
R. Klokov and V. Lempitsky · 2017
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A structured self-attentive sentence embedding
Z. Lin, M. Feng, C. N. d. Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio · 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
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Segcloud: Semantic segmentation of 3d point clouds
L. Tchapmi, C. Choy, I. Armeni, J. Y. Gwak, and S. Savarese · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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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
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
B. Graham, M. Engelcke, and L. van der Maaten · 2018
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Recurrent slice networks for 3d segmentation of point clouds
Q. Huang, W. Wang, and U. a. Neumann · 2018
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Large-scale point cloud semantic segmentation with superpoint graphs
L. Landrieu and M. Simonovsky · 2018
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Pointgrid: A deep network for 3d shape understanding
T. Le and D. Ye · 2018
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So-net: Self-organizing network for point cloud analysis
J. Li, B. M. Chen, and G. H. Lee · 2018
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Pointcnn: Convolution on x-transformed points
Y. Li, R. Bu, M. Sun, and B. Chen · 2018
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Rs-net: Regression-segmentation 3d cnn for synthesis of full resolution missing brain mri in the presence of tumours
R. Mehta and T. Arbel · 2018
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Tangent convolutions for dense prediction in 3d
M. Tatarchenko, J. Park, V. Koltun, and Q. Y. Zhou · 2018
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Local spectral graph convolution for point set feature learning
C. Wang, B. Samari, and K. Siddiqi · 2018
Cited alongside, same era.
Sgpn: Similarity group proposal network for 3d point cloud instance segmentation
W. Wang, R. Yu, Q. Huang, and U. Neumann · 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.
Spidercnn: Deep learning on point sets with parameterized convolutional filters
Y. Xu, T. Fan, M. Xu, Z. Long, and Q. Yu · 2018
Cited alongside, same era.
Second: Sparsely embedded convolutional detection
Y. Yan, Y. Mao, and B. Li · 2018
Cited alongside, same era.
Shellnet: Efficient point cloud convolutional neural networks using concentric shells statistics
Z. Zhang, B. S. Hua, and S. K. Yeung · 2019
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Pointweb: Enhancing local neighborhood features for point cloud processing
H. Zhao, L. Jiang, C. W. Fu, and J. Jia · 2019
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Pointweb: Enhancing local neighborhood features for point cloud processing
H. Zhao, L. Jiang, C.-W. Fu, and J. Jia · 2019
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Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis
C. Chen, G. Li, R. Xu, T. Chen, M. Wang, and L. Lin · 2020
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N. Engel, V. Belagiannis, and K. Dietmayer · 2020
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3d recurrent neural networks with context fusion for point cloud semantic segmentation
X. Ye, J. Li, H. Huang, L. Du, and X. Zhang · 2018
Cited alongside, same era.
Open3D: A modern library for 3D data processing
Q.-Y. Zhou, J. Park, and V. Koltun · 2018
Cited alongside, same era.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
Cited alongside, same era.
Semantickitti: A dataset for semantic scene understanding of lidar sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
Cited alongside, same era.
4d spatio-temporal convnets: Minkowski convolutional neural networks
C. Choy, J. Y. Gwak, and S. Savarese · 2019
Cited alongside, same era.
Local relation networks for image recognition
H. Hu, Z. Zhang, Z. Xie, and S. Lin · 2019
Cited alongside, same era.
Se(3)-transformers: 3d roto-translation equivariant attention networks
F. Fuchs, D. E. Worrall, V. Fischer, and M. Welling · 2020
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Point cloud oversegmentation with graph-structured deep metric learning
L. Landrieu and M. Boussaha · 2020
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Searching efficient 3d architectures with sparse point-voxel convolution
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han · 2020
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Grid-gcn for fast and scalable point cloud learning
Q. Xu, X. Sun, C.-Y. Wu, P. Wang, and U. Neumann · 2020
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Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling
X. Yan, C. Zheng, Z. Li, S. Wang, and S. Cui · 2020
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Exploring self-attention for image recognition
H. Zhao, J. Jia, and V. K. and · 2020
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Hapgn: Hierarchical attentive pooling graph network for point cloud segmentation
C. Chen, S. Qian, Q. Fang, and C. Xu · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2021
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Boundary-aware geometric encoding for semantic segmentation of point clouds
J. Gong, J. Xu, X. Tan, J. Zhou, Y. Qu, Y. Xie, and L. Ma · 2021
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Pct: Point cloud transformer
M.-H. Guo, J.-X. Cai, Z.-N. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo · 2021
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Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion
Q. Shi, S. Anwar, and N. Barnes · 2021
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Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer
P. Xiang, X. Wen, Y. Liu, Y. Cao, P. Wan, W. Zheng, and Z. Han · 2021
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Investigate indistinguishable points in semantic segmentation of 3d point cloud
M. Xu, Z. Zhou, J. Zhang, and Y. Qiao · 2021
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Pointr: Diverse point cloud completion with geometry-aware transformers
X. Yu, Y. Rao, Z. Wang, Z. Liu, J. Lu, and J. Zhou · 2021
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, and J. Lu · 2021
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Point transformer
H. Zhao, L. Jiang, J. Jia, P. Torr, and V. Koltun · 2021
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