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The point cloud learning community witnesses a modeling shift from CNNs to Transformers, where pure Transformer architectures have achieved top accuracy on the major learning benchmarks.
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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Joint 2d-3d-semantic data for indoor scene understanding
I. Armeni, S. Sax, A. R. Zamir, and S. Savarese · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 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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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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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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Recurrent slice networks for 3d segmentation of point clouds
Q. Huang, W. Wang, and U. a. Neumann · 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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Self-attention with relative position representations
P. Shaw, J. Uszkoreit, and A. Vaswani · 2018
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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
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Spidercnn: Deep learning on point sets with parameterized convolutional filters
Y. Xu, T. Fan, M. Xu, Z. Long, and Q. Yu · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
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4d spatio-temporal convnets: Minkowski convolutional neural networks
C. Choy, J. Y. Gwak, and S. Savarese · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Z. Dai, Z. Yang, Y. Yang, J. G. Carbonell, Q. V. Le, and R. Salakhutdinov · 2019
Cited alongside, same era.
Expectation-maximization attention networks for semantic segmentation
X. Li, Z. Zhong, J. Wu, Y. Yang, and H. Liu · 2019
Cited alongside, same era.
Relation-shape convolutional neural network for point cloud analysis
Y. Liu, B. Fan, S. Xiang, and C. Pan · 2019
Cited alongside, same era.
Point-voxel cnn for efficient 3d deep learning
Z. Liu, H. Tang, Y. Lin, and S. Han · 2019
Cited alongside, same era.
Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li · 2019
Cited alongside, same era.
Kpconv: Flexible and deformable convolution for point clouds
H. Thomas, C. R. Qi, J. E. Deschaud, B. Marcotegui, and L. J. Guibas · 2019
Crossvit: Cross-attention multi-scale vision transformer for image classification
C. Chen, Q. Fan, and R. Panda · 2021
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Rethinking attention with performers
K. M. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlós, P. Hawkins, J. Q. Davis, A. Mohiuddin, L. Kaiser, D. B. Belanger, L. J. Colwell, and A. Weller · 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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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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Point cloud upsampling via disentangled refinement
R. Li, X. Li, P. Heng, and C. Fu · 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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Cited alongside, same era.
Graph attention convolution for point cloud semantic segmentation
L. Wang, Y. Huang, Y. Hou, S. Zhang, and J. Shan · 2019
Cited alongside, same era.
Voxsegnet: Volumetric cnns for semantic part segmentation of 3d shapes
Z. Wang and F. Lu · 2019
Cited alongside, same era.
Learning object bounding boxes for 3d instance segmentation on point clouds
B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, and N. Trigoni · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le · 2019
Cited alongside, same era.
Acfnet: Attentional class feature network for semantic segmentation
F. Zhang, Y. Chen, Z. Li, Z. Hong, J. Liu, F. Ma, J. Han, and E. Ding · 2019
Cited alongside, same era.
Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features
K. Zhang, M. Hao, J. Wang, C. D. Silva, and C. Fu · 2019
Cited alongside, same era.
Closest in time.
Z. Liu, J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu · 2021
Closest in time.
Variational relational point completion network
L. Pan, X. Chen, Z. Cai, J. Zhang, H. Zhao, S. Yi, and Z. Liu · 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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Scaling local self-attention for parameter efficient visual backbones
A. Vaswani, P. Ramachandran, A. Srinivas, N. Parmar, B. Hechtman, and J. Shlens · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
W. Wang, E. Xie, X. Li, D. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao · 2021
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Crossformer: A versatile vision transformer based on cross-scale attention
W. Wang, L. Yao, L. Chen, D. Cai, X. He, and W. Liu · 2021
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Rethinking and improving relative position encoding for vision transformer
K. Wu, H. Peng, M. Chen, J. Fu, and H. Chao · 2021
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Walk in the cloud: Learning curves for point clouds shape analysis
T. Xiang, C. Zhang, Y. Song, J. Yu, and W. Cai · 2021
Closest in time.
Pointr: Diverse point cloud completion with geometry-aware transformers
X. Yu, Y. Rao, Z. Wang, Z. Liu, J. Lu, and J. Zhou · 2021
Closest in time.
Graph-pbn: Graph-based parallel branch network for efficient point cloud learning
C. Zhang, H. Chen, H. Wan, P. Yang, and Z. Wu · 2021
Closest in time.
Pvt: Point-voxel transformer for 3d deep learning
C. Zhang, H. Wan, S. Liu, X. Shen, and Z. Wu · 2021
Closest in time.
Point transformer
H. Zhao, L. Jiang, J. Jia, P. Torr, and V. Koltun · 2021
Closest in time.