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Discrete point cloud objects lack sufficient shape descriptors of 3D geometries.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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VoxNet: A 3D convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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Multi-view convolutional neural networks for 3D shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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RotationNet: Learning object classification using unsupervised viewpoint estimation
Asako Kanezaki, Y Matsushita, and Y Nishida · 2016
Earlier work this paper cites.
FPNN: Field probing neural networks for 3D data
Yangyan Li, Soeren Pirk, Hao Su, Charles R Qi, and Leonidas J Guibas · 2016
Earlier work this paper cites.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
Earlier work this paper cites.
Volumetric and multi-view CNNs for object classification on 3D data
Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
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DeepShape: Deep-learned shape descriptor for 3d shape retrieval
Jin Xie, Guoxian Dai, Fan Zhu, Edward K Wong, and Yi Fang · 2016
Earlier work this paper cites.
A scalable active framework for region annotation in 3D shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
Earlier work this paper cites.
Escape from cells: Deep kd-networks for the recognition of 3D point cloud models
Roman Klokov and Victor Lempitsky · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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OctNet: Learning deep 3D representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 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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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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So-Net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M Chen, and Gim Hee Lee · 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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Attention U-Net: Learning where to look for the pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al · 2018
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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PCAN: 3D attention map learning using contextual information for point cloud based retrieval
Wenxiao Zhang and Chunxia Xiao · 2019
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Pointweb: Enhancing local neighborhood features for point cloud processing
Hengshuang Zhao, Li Jiang, Chi-Wing Fu, and Jiaya Jia · 2019
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Local-area-learning network: Meaningful local areas for efficient point cloud analysis
Qendrim Bytyqi, Nicola Wolpert, and Elmar Schömer · 2020
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Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu · 2020
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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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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Hongyang Gao and Shuiwang Ji · 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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Octree guided cnn with spherical kernels for 3D point clouds
Huan Lei, Naveed Akhtar, and Ajmal Mian · 2019
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Point2Sequence: Learning the shape representation of 3D point clouds with an attention-based sequence to sequence network
Xinhai Liu, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker · 2019
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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
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JSENet: Joint semantic segmentation and edge detection network for 3D point clouds
Zeyu Hu, Mingmin Zhen, Xuyang Bai, Hongbo Fu, and Chiew-lan Tai · 2020
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Meshwalker: Deep mesh understanding by random walks
Alon Lahav and Ayellet Tal · 2020
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Point2skeleton: Learning skeletal representations from point clouds
Cheng Lin, Changjian Li, Yuan Liu, Nenglun Chen, Yi-King Choi, and Wenping Wang · 2020
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A closer look at local aggregation operators in point cloud analysis
Ze Liu, Han Hu, Yue Cao, Zheng Zhang, and Xin Tong · 2020
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Geometry sharing network for 3d point cloud classification and segmentation
Mingye Xu, Zhipeng Zhou, and Yu Qiao · 2020
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Grid-GCN for fast and scalable point cloud learning
Qiangeng Xu, Xudong Sun, Cho-Ying Wu, Panqu Wang, and Ulrich Neumann · 2020
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PointASNL: Robust point clouds processing using nonlocal neural networks with adaptive sampling
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, and Shuguang Cui · 2020
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Shape-oriented convolution neural network for point cloud analysis
Chaoyi Zhang, Yang Song, Lina Yao, and Weidong Cai · 2020
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Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi · 2021
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Exploiting edge-oriented reasoning for 3d point-based scene graph analysis
Chaoyi Zhang, Jianhui Yu, Yang Song, and Weidong Cai · 2021
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