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Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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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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Fitnets: Hints for thin deep nets
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Very deep convolutional networks for large-scale image recognition
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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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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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 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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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Submanifold sparse convolutional networks
Benjamin Graham and Laurens van der Maaten · 2017
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Mask r-cnn
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Zehao Huang and Naiyan Wang · 2017
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Mimicking very efficient network for object detection
Quanquan Li, Shengying Jin, and Junjie Yan · 2017
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Focal loss for dense object detection
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 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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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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Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2019
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Fast point r-cnn
Yilun Chen, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
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Knowledge transfer via distillation of activation boundaries formed by hidden neurons
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi · 2019
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Learning lightweight lane detection cnns by self attention distillation
Yuenan Hou, Zheng Ma, Chunxiao Liu, and Chen Change Loy · 2019
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Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Junjie Yan, and Xiaolin Hu · 2019
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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
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
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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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Openpcdet: An open-source toolbox for 3d object detection from point clouds
OpenPCDet Development Team · 2020
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Pillar-based object detection for autonomous driving
Yue Wang, Alireza Fathi, Abhijit Kundu, David A Ross, Caroline Pantofaru, Tom Funkhouser, and Justin Solomon · 2020
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Multi-frame to single-frame: Knowledge distillation for 3d object detection
Yue Wang, Alireza Fathi, Jiajun Wu, Thomas Funkhouser, and Justin Solomon · 2020
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Structured knowledge distillation for semantic segmentation
Yifan Liu, Ke Chen, Chris Liu, Zengchang Qin, Zhenbo Luo, and Jingdong Wang · 2019
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Lasernet: An efficient probabilistic 3d object detector for autonomous driving
Gregory P Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, and Carl K Wellington · 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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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Distilling object detectors with fine-grained feature imitation
Tao Wang, Li Yuan, Xiaopeng Zhang, and Jiashi Feng · 2019
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Std: Sparse-to-dense 3d object detector for point cloud
Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
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Cross-layer distillation with semantic calibration
Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Zhe Wang, Yan Feng, and Chun Chen · 2021
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General instance distillation for object detection
Xing Dai, Zeren Jiang, Zhao Wu, Yiping Bao, Zhicheng Wang, Si Liu, and Erjin Zhou · 2021
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Liga-stereo: Learning lidar geometry aware representations for stereo-based 3d detector
Xiaoyang Guo, Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2021
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Benchmarking detection transfer learning with vision transformers
Yanghao Li, Saining Xie, Xinlei Chen, Piotr Dollar, Kaiming He, and Ross Girshick · 2021
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3d-to-2d distillation for indoor scene parsing
Zhengzhe Liu, Xiaojuan Qi, and Chi-Wing Fu · 2021
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Multi-scale aligned distillation for low-resolution detection
Lu Qi, Jason Kuen, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, and Jiaya Jia · 2021
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Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2021
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Rsn: Range sparse net for efficient, accurate lidar 3d object detection
Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, and Dragomir Anguelov · 2021
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Object dgcnn: 3d object detection using dynamic graphs
Yue Wang and Justin M Solomon · 2021
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Categorical relation-preserving contrastive knowledge distillation for medical image classification
Xiaohan Xing, Yuenan Hou, Hang Li, Yixuan Yuan, Hongsheng Li, and Max Q-H Meng · 2021
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Focal and global knowledge distillation for detectors
Zhendong Yang, Zhe Li, Xiaohu Jiang, Yuan Gong, Zehuan Yuan, Danpei Zhao, and Chun Yuan · 2021
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Center-based 3d object detection and tracking
Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl · 2021
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Improving object detection by label assignment distillation
Chuong H Nguyen, Thuy C Nguyen, Tuan N Tang, and Nam LH Phan · 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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