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We introduce Position Adaptive Convolution (PAConv), a generic convolution operation for 3D point cloud processing.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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Towards 3d point cloud based object maps for household environments
Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Mihai Dolha, and Michael Beetz · 2008
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Pearson correlation coefficient
Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen · 2009
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik G. 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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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
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Dynamic filter networks
Bert De Brabandere, Xu Jia, Tinne Tuytelaars, and Luc Van Gool · 2016
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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
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Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks
Alexandre Boulch, Bertrand Le Saux, and Nicolas Audebert · 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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Deep projective 3d semantic segmentation
Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg · 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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3d graph neural networks for rgbd semantic segmentation
Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, and Raquel Urtasun · 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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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Segcloud: Semantic segmentation of 3d point clouds
Lyne P. Tchapmi, Christopher B. Choy, Iro Armeni, JunYoung Gwak, and Silvio Savarese · 2017
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Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
Li Yi, Hao Su, Xingwen Guo, and Leonidas J. Guibas · 2017
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Point convolutional neural networks by extension operators
Matan Atzmon, Haggai Maron, and Yaron Lipman · 2018
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3dmfv: Three-dimensional point cloud classification in real-time using convolutional neural networks
Y. Ben-Shabat, M. Lindenbaum, and A. Fischer · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 2018
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Flex-convolution (million-scale point-cloud learning beyond grid-worlds)
Fabian Groh, Patrick Wieschollek, and Hendrik P. A. Lensch · 2018
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Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
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Recurrent slice networks for 3d segmentation of point clouds
Qiangui Huang, Weiyue Wang, and Ulrich Neumann · 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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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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Vv-net: Voxel vae net with group convolutions for point cloud segmentation
Hsien-Yu Meng, Lin Gao, Yu-Kun Lai, and Dinesh Manocha · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 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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Graph attention convolution for point cloud semantic segmentation
Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, and Jie Shan · 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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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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SPLATNet: Sparse lattice networks for point cloud processing
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, and Jan Kautz · 2018
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Tangent convolutions for dense prediction in 3d
M. Tatarchenko, J. Park, V. Koltun, and Q. Zhou · 2018
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Local spectral graph convolution for point set feature learning
Chu Wang, Babak Samari, and Kaleem Siddiqi · 2018
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Deep parametric continuous convolutional neural networks
S. Wang, S. Suo, W. Ma, A. Pokrovsky, and R. Urtasun · 2018
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Spidercnn: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
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Pointconv: Deep convolutional networks on 3d point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
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Condconv: Conditionally parameterized convolutions for efficient inference
Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam · 2019
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Convpoint: Continuous convolutions for point cloud processing
Alexandre Boulch · 2020
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Deformable kernels: Adapting effective receptive fields for object deformation
Hang Gao, Xizhou Zhu, Stephen Lin, and Jifeng Dai · 2020
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Point2node: Correlation learning of dynamic-node for point cloud feature modeling
Wenkai Han, Chenglu Wen, Cheng Wang, Xin Li, and Qing Li · 2020
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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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Seggcn: Efficient 3d point cloud segmentation with fuzzy spherical kernel
Huan Lei, Naveed Akhtar, and Ajmal Mian · 2020
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Fpconv: Learning local flattening for point convolution
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, and Xiaoguang Han · 2020
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Convolution in the cloud: Learning deformable kernels in 3d graph convolution networks for point cloud analysis
Zhi-Hao Lin, Sheng-Yu Huang, and Yu-Chiang Frank 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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Cn: Channel normalization for point cloud recognition
Zetong Yang, Yanan Sun, Shu Liu, Xiaojuan Qi, and Jiaya Jia · 2020
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Lambdanetworks: Modeling long-range interactions without attention
Irwan Bello · 2021
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Mutian Xu, Junhao Zhang, Zhipeng Zhou, Mingye Xu, Xiaojuan Qi, and Yu Qiao · 2021
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Investigate indistinguishable points in semantic segmentation of 3d point cloud
Mingye Xu, Zhipeng Zhou, Junhao Zhang, and Yu Qiao · 2021
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