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Most prior work represents the shapes of point clouds by coordinates.
The ball-pivoting algorithm for surface reconstruction
Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Claudio Silva, and Gabriel Taubin · 1999
Earlier work this paper cites.
Poisson surface reconstruction
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
Earlier work this paper cites.
Curvature estimation of 3d point cloud surfaces through the fitting of normal section curvatures
Xiaopeng Zhang, Hongjun Li, Zhanglin Cheng, et al · 2008
Earlier work this paper cites.
Umbrella curvature: a new curvature estimation method for point clouds
A Foorginejad and K Khalili · 2014
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
Earlier work this paper cites.
Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
3d semantic parsing of large-scale indoor spaces
Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese · 2016
Earlier work this paper cites.
Multi-view 3d object retrieval with deep embedding network
Haiyun Guo, Jinqiao Wang, Yue Gao, Jianqiang Li, and Hanqing Lu · 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.
Deep sliding shapes for amodal 3d object detection in rgb-d images
Shuran Song and Jianxiong Xiao · 2016
Earlier work this paper cites.
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 survey of surface reconstruction from point clouds
Matthew Berger, Andrea Tagliasacchi, Lee M Seversky, Pierre Alliez, Gael Guennebaud, Joshua A Levine, Andrei Sharf, and Claudio T Silva · 2017
Earlier work this paper cites.
Multi-view 3d object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
Earlier work this paper cites.
Gvcnn: Group-view convolutional neural networks for 3d shape recognition
Yifan Feng, Zizhao Zhang, Xibin Zhao, Rongrong Ji, and Yue Gao · 2018
Earlier work this paper cites.
Multiresolution tree networks for 3d point cloud processing
Matheus Gadelha, Rui Wang, and Subhransu Maji · 2018
Earlier work this paper cites.
3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens Van Der Maaten · 2018
Earlier work this paper cites.
Seqviews2seqlabels: Learning 3d global features via aggregating sequential views by rnn with attention
Zhizhong Han, Mingyang Shang, Zhenbao Liu, Chi-Man Vong, Yu-Shen Liu, Matthias Zwicker, Junwei Han, and CL Philip Chen · 2018
Earlier work this paper cites.
Monte carlo convolution for learning on non-uniformly sampled point clouds
Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, and Timo Ropinski · 2018
Cited alongside, same era.
Joint 3d proposal generation and object detection from view aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L Waslander · 2018
Cited alongside, same era.
Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
Cited alongside, same era.
Deep continuous fusion for multi-sensor 3d object detection
Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun · 2018
Cited alongside, same era.
Frustum pointnets for 3d object detection from rgb-d data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2018
Cited alongside, same era.
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
Neural implicit embedding for point cloud analysis
Kent Fujiwara and Taiichi Hashimoto · 2020
Later among the works it cites.
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
Later among the works it cites.
Samplenet: Differentiable point cloud sampling
Itai Lang, Asaf Manor, and Shai Avidan · 2020
Later among the works it cites.
Going deeper with lean point networks
Eric-Tuan Le, Iasonas Kokkinos, and Niloy J Mitra · 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
Later among the works it cites.
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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Cited alongside, same era.
Deep parametric continuous convolutional neural networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, and Raquel Urtasun · 2018
Cited alongside, same era.
Multi-level fusion based 3d object detection from monocular images
Bin Xu and Zhenzhong Chen · 2018
Cited alongside, same era.
Spidercnn: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
Cited alongside, same era.
Pixor: Real-time 3d object detection from point clouds
Bin Yang, Wenjie Luo, and Raquel Urtasun · 2018
Cited alongside, same era.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
Cited alongside, same era.
4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
Cited alongside, same era.
Later among the works it cites.
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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Adaptive hierarchical down-sampling for point cloud classification
Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani, Bingbing Liu, and Jun Luo · 2020
Later among the works it cites.
Imvotenet: Boosting 3d object detection in point clouds with image votes
Charles R Qi, Xinlei Chen, Or Litany, and Leonidas J Guibas · 2020
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Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2020
Later among the works it cites.
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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Cn: Channel normalization for point cloud recognition
Zetong Yang, Yanan Sun, Shu Liu, Xiaojuan Qi, and Jiaya Jia · 2020
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H3dnet: 3d object detection using hybrid geometric primitives
Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang · 2020
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Back-tracing representative points for voting-based 3d object detection in point clouds
Bowen Cheng, Lu Sheng, Shaoshuai Shi, Ming Yang, and Dong Xu · 2021
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Mvtn: Multi-view transformation network for 3d shape recognition
Abdullah Hamdi, Silvio Giancola, and Bernard Ghanem · 2021
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Group-free 3d object detection via transformers
Ze Liu, Zheng Zhang, Yue Cao, Han Hu, and Xin Tong · 2021
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An end-to-end transformer model for 3d object detection
Ishan Misra, Rohit Girdhar, and Armand Joulin · 2021
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Differentiable convolution search for point cloud processing
Xing Nie, Yongcheng Liu, Shaohong Chen, Jianlong Chang, Chunlei Huo, Gaofeng Meng, Qi Tian, Weiming Hu, and Chunhong Pan · 2021
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Learning inner-group relations on point clouds
Haoxi Ran, Wei Zhuo, Jun Liu, and Li Lu · 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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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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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Adaptive graph convolution for point cloud analysis
Haoran Zhou, Yidan Feng, Mingsheng Fang, Mingqiang Wei, Jing Qin, and Tong Lu · 2021
Later among the works it cites.