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Monocular 3D object detection is an important task for autonomous driving considering its advantage of low cost.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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3d object proposals for accurate object class detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2015
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Fast r-cnn
Ross Girshick · 2015
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Densebox: Unifying landmark localization with end to end object detection
Lichao Huang, Yi Yang, Yafeng Deng, and Yinan Yu · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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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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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 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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3d bounding box estimation using deep learning and geometry
Arsalan Mousavian, Dragomir Anguelov, John Flynn, and Jana Kosecka · 2017
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Yolo9000: Better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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Orthographic feature transform for monocular 3d object detection
Thomas Roddick, Alex Kendall, and Roberto Cipolla · 2018
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Multi-level fusion based 3d object detection from monocular images
Bin Xu and Zhenzhong Chen · 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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M3d-rpn: Monocular 3d region proposal network for object detection
Garrick Brazil and Xiaoming Liu · 2019
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
Cited alongside, same era.
Kinematic 3d object detection in monocular video
Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu, and Bernt Schiele · 2020
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Monopair: Monocular 3d object detection using pairwise spatial relationships
Yongjian Chen, Lei Tai, Kai Sun, and Mingyang Li · 2020
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MMDetection3D: OpenMMLab next-generation platform for general 3D object detection
MMDetection3D Contributors · 2020
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Learning depth-guided convolutions for monocular 3d object detection
Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, and Ping Luo · 2020
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Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving
Peixuan Li, Huaici Zhao, Pengfei Liu, and Feidao Cao · 2020
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Centerfusion: Center-based radar and camera fusion for 3d object detection
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Eskil Jörgensen, Christopher Zach, and Fredrik Kahl · 2019
Cited alongside, same era.
Pointpillars: Fast encoders for object detection from point clouds
Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
Cited alongside, same era.
Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape
Fabian Manhardt, Wadim Kehl, and Adrien Gaidon · 2019
Cited alongside, same era.
Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
Cited alongside, same era.
Disentangling monocular 3d object detection
Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder · 2019
Cited alongside, same era.
Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
Cited alongside, same era.
Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q. Weinberger · 2019
Cited alongside, same era.
Ramin Nabati and Hairong Qi · 2020
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End-to-end pseudo-lidar for image-based 3d object detection
Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger, and Wei-Lun Chao · 2020
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Reconfigurable voxels: A new representation for lidar-based point clouds
Tai Wang, Xinge Zhu, and Dahua Lin · 2020
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Task-aware monocular depth estimation for 3d object detection
Xinlong Wang, Wei Yin, Tao Kong, Yuning Jiang, Lei Li, and Chunhua Shen · 2020
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Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2020
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Ssn: Shape signature networks for multi-class object detection from point clouds
Xinge Zhu, Yuexin Ma, Tai Wang, Yan Xu, Jianping Shi, and Dahua Lin · 2020
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Categorical depth distributionnetwork for monocular 3d object detection
Cody Reading, Ali Harakeh, Julia Chae, and Steven L. Waslander · 2021
Closest in time.
Probabilistic and geometric depth: Detecting objects in perspective
Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin · 2021
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Cylindrical and asymmetrical 3d convolution networks for lidar segmentation
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin · 2021
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