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Recently, 3D point cloud classification has made significant progress with the help of many datasets.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
Roman Klokov and Victor Lempitsky · 2017
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Monte carlo convolution for learning on non-uniformly sampled point clouds
Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, and Timo Ropinski · 2018
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Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Frustum pointnets for 3d object detection from rgb-d data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2018
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Mining point cloud local structures by kernel correlation and graph pooling
Yiru Shen, Chen Feng, Yaoqing Yang, and Dong Tian · 2018
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Attentional shapecontextnet for point cloud recognition
Saining Xie, Sainan Liu, Zeyu Chen, and Zhuowen Tu · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Learning to sample
Oren Dovrat, Itai Lang, and Shai Avidan · 2019
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Hierarchical point-edge interaction network for point cloud semantic segmentation
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, and Jiaya Jia · 2019
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A-cnn: Annularly convolutional neural networks on point clouds
Artem Komarichev, Zichun Zhong, and Jing Hua · 2019
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Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Pointconv: Deep convolutional networks on 3d point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
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Modeling point clouds with self-attention and gumbel subset sampling
Jiancheng Yang, Qiang Zhang, Bingbing Ni, Linguo Li, Jinxian Liu, Mengdie Zhou, and Qi Tian · 2019
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Unsupervised feature learning for point cloud understanding by contrasting and clustering using graph convolutional neural networks
Ling Zhang and Zhigang Zhu · 2019
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Rotation invariant convolutions for 3d point clouds deep learning
Zhiyuan Zhang, Binh-Son Hua, David W Rosen, and Sai-Kit Yeung · 2019
Cited alongside, same era.
Pointweb: Enhancing local neighborhood features for point cloud processing
Hengshuang Zhao, Li Jiang, Chi-Wing Fu, and Jiaya Jia · 2019
Cited alongside, same era.
Dup-net: Denoiser and upsampler network for 3d adversarial point clouds defense
Hang Zhou, Kejiang Chen, Weiming Zhang, Han Fang, Wenbo Zhou, and Nenghai Yu · 2019
Cited alongside, same era.
Pointmixup: Augmentation for point clouds
Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang, and Cees GM Snoek · 2020
Cited alongside, same era.
Randla-net: Efficient semantic segmentation of large-scale point clouds
Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham · 2020
Cited alongside, same era.
Samplenet: Differentiable point cloud sampling
Unsupervised point cloud pre-training via occlusion completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matt J Kusner · 2021
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Walk in the cloud: Learning curves for point clouds shape analysis
Tiange Xiang, Chaoyi Zhang, Yang Song, Jianhui Yu, and Weidong Cai · 2021
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Triangle-net: Towards robustness in point cloud learning
Chenxi Xiao and Juan Wachs · 2021
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Self-supervised pretraining of 3d features on any point-cloud
Zaiwei Zhang, Rohit Girdhar, Armand Joulin, and Ishan Misra · 2021
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Arbitrary point cloud upsampling with spherical mixture of gaussians
Anthony Dell’Eva, Marco Orsingher, and Massimo Bertozzi · 2022
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Itai Lang, Asaf Manor, and Shai Avidan · 2020
Cited alongside, same era.
Pointaugment: an auto-augmentation framework for point cloud classification
Ruihui Li, Xianzhi Li, Pheng-Ann Heng, and Chi-Wing Fu · 2020
Cited alongside, same era.
Deep learning for 3d point cloud understanding: a survey
Haoming Lu and Humphrey Shi · 2020
Cited alongside, same era.
Adaptive hierarchical down-sampling for point cloud classification
Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani, Bingbing Liu, and Jun Luo · 2020
Cited alongside, same era.
Pointcleannet: Learning to denoise and remove outliers from dense point clouds
Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J Mitra, and Maks Ovsjanikov · 2020
Cited alongside, same era.
Masknet: A fully-convolutional network to estimate inlier points
Vinit Sarode, Animesh Dhagat, Rangaprasad Arun Srivatsan, Nicolas Zevallos, Simon Lucey, and Howie Choset · 2020
Cited alongside, same era.
Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany · 2020
Cited alongside, same era.
Kexue Fu, Peng Gao, ShaoLei Liu, Renrui Zhang, Yu Qiao, and Manning Wang · 2022
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Masked autoencoders in 3d point cloud representation learning
Jincen Jiang, Xuequan Lu, Lizhi Zhao, Richard Dazeley, and Meili Wang · 2022
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Masked discrimination for self-supervised learning on point clouds
Haotian Liu, Mu Cai, and Yong Jae Lee · 2022
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Rethinking network design and local geometry in point cloud: A simple residual mlp framework
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu · 2022
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Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan · 2022
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Surface representation for point clouds
Haoxi Ran, Jun Liu, and Chengjie Wang · 2022
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Benchmarking and analyzing point cloud classification under corruptions
Jiawei Ren, Liang Pan, and Ziwei Liu · 2022
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P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting
Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou, and Jiwen Lu · 2022
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu · 2022
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Pointclip v2: Adapting clip for powerful 3d open-world learning
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyao Zeng, Shanghang Zhang, and Peng Gao · 2022
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Pointgpt: Auto-regressively generative pre-training from point clouds
Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu, Li Yuan, and Yufeng Yue · 2023
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Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning?
Runpei Dong, Zekun Qi, Linfeng Zhang, Junbo Zhang, Jianjian Sun, Zheng Ge, Li Yi, and Kaisheng Ma · 2023
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Meta architecture for point cloud analysis
Haojia Lin, Xiawu Zheng, Lijiang Li, Fei Chao, Shanshan Wang, Yan Wang, Yonghong Tian, and Rongrong Ji · 2023
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Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders
Renrui Zhang, Liuhui Wang, Yu Qiao, Peng Gao, and Hongsheng Li · 2023
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