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Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
Statistical theory of extreme values and some practical applications: a series of lectures , volume 33
Emil Julius Gumbel · 1954
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3d iou-net: Iou guided 3d object detector for point clouds
Jiale Li, Shujie Luo, Ziqi Zhu, Hang Dai, Andrey S Krylov, Yong Ding, and Ling Shao · 2004
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Multiple 3d object tracking for augmented reality
Youngmin Park, Vincent Lepetit, and Woontack Woo · 2008
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q Weinberger · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 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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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Human centric spatio-temporal action localization
Jianwen Jiang, Yu Cao, Lin Song, Shiwei Zhang, Yunkai Li, Ziyao Xu, Qian Wu, Chuang Gan, Chi Zhang, and Gang Yu · 2018
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Accelerating convolutional networks via global & dynamic filter pruning
Shaohui Lin, Rongrong Ji, Yuchao Li, Yongjian Wu, Feiyue Huang, and Baochang Zhang · 2018
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Hydranets: Specialized dynamic architectures for efficient inference
Ravi Teja Mullapudi, William R Mark, Noam Shazeer, and Kayvon Fatahalian · 2018
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Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E Gonzalez · 2018
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Blockdrop: Dynamic inference paths in residual networks
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S Davis, Kristen Grauman, and Rogerio Feris · 2018
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Second: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
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Fast point r-cnn
Yilun Chen, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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Associate-3ddet: Perceptual-to-conceptual association for 3d point cloud object detection
Liang Du, Xiaoqing Ye, Xiao Tan, Jianfeng Feng, Zhenbo Xu, Errui Ding, and Shilei Wen · 2020
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Structure aware single-stage 3d object detection from point cloud
Chenhang He, Hui Zeng, Jianqiang Huang, Xian-Sheng Hua, and Lei Zhang · 2020
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Tanet: Robust 3d object detection from point clouds with triple attention
Zhe Liu, Xin Zhao, Tengteng Huang, Ruolan Hu, Yu Zhou, and Xiang Bai · 2020
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Point-gnn: Graph neural network for 3d object detection in a point cloud
Weijing Shi and Raj Rajkumar · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al · 2020
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Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
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Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
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Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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Std: Sparse-to-dense 3d object detector for point cloud
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Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
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Dynamic convolutions: Exploiting spatial sparsity for faster inference
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Wu Zheng, Weiliang Tang, Sijin Chen, Li Jiang, and Chi-Wing Fu · 2020
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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Vic-net: Voxelization information compensation network for point cloud 3d object detection
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Hvpr: Hybrid voxel-point representation for single-stage 3d object detection
Jongyoun Noh, Sanghoon Lee, and Bumsub Ham · 2021
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Dynamic grained encoder for vision transformers
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End-to-end object detection with fully convolutional network
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Sasa: Semantics-augmented set abstraction for point-based 3d object detection
Chen Chen, Zhe Chen, Jing Zhang, and Dacheng Tao · 2022
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Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds
Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, and Yulan Guo · 2022
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