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This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation.
The perception of the visual world
James J. Gibson · 1950
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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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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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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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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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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Monocular 3d object detection for autonomous driving
Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, 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 B. Girshick, Kaiming He, and Piotr Dollár · 2017
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Modeling visual context is key to augmenting object detection datasets
Nikita Dvornik, Julien Mairal, and Cordelia Schmid · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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MVDepthNet: real-time multiview depth estimation neural network
Kaixuan Wang and Shaojie Shen · 2018
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Second: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
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M3d-rpn: Monocular 3d region proposal network for object detection
Garrick Brazil and Xiaoming Liu · 2019
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How do neural networks see depth in single images?
Tom van Dijk and Guido de Croon · 2019
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Instaboost: Boosting instance segmentation via probability map guided copy-pasting
Hao-Shu Fang, Jianhua Sun, Runzhong Wang, Minghao Gou, Yong-Lu Li, and Cewu Lu · 2019
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Consistency-based semi-supervised learning for object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, and Nojun Kwak · 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
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Stereo r-cnn based 3d object detection for autonomous driving
Peiliang Li, Xiaozhi Chen, and Shaojie Shen · 2019
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Deep fitting degree scoring network for monocular 3d object detection
Lijie Liu, Jiwen Lu, Chunjing Xu, Qi Tian, and Jie Zhou · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Monogrnet: A geometric reasoning network for monocular 3d object localization
Zengyi Qin, Jinglu Wang, and Yan Lu · 2019
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Disentangling monocular 3d object detection
Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder · 2019
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Data augmentation for object detection via progressive and selective instance-switching
Reinforced axial refinement network for monocular 3d object detection
Lijie Liu, Chufan Wu, Jiwen Lu, Lingxi Xie, Jie Zhou, and Qi Tian · 2020
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Smoke: Single-stage monocular 3d object detection via keypoint estimation
Zechen Liu, Zizhang Wu, and Roland Tóth · 2020
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Multi-modality cut and paste for 3d object detection
Wenwei Zhang, Zhe Wang, and Chen Change Loy · 2020
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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V. Le · 2020
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Unbiased mean teacher for cross-domain object detection
Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan · 2021
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Hao Wang, Qilong Wang, Fan Yang, Weiqi Zhang, and Wangmeng Zuo · 2019
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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
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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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 · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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nuscenes: A multimodal dataset for autonomous driving
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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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Peixuan Li and Huaici Zhao · 2021
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Autoshape: Real-time shape-aware monocular 3d object detection
Zongdai Liu, Dingfu Zhou, Feixiang Lu, Jin Fang, and Liangjun Zhang · 2021
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Geometry uncertainty projection network for monocular 3d object detection
Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Junjie Yan, and Wanli Ouyang · 2021
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M3dssd: Monocular 3d single stage object detector
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Delving into localization errors for monocular 3d object detection
Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, and Wanli Ouyang · 2021
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Is pseudo-lidar needed for monocular 3d object detection?
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Categorical depth distribution network for monocular 3d object detection
Cody Reading, Ali Harakeh, Julia Chae, and Steven L. Waslander · 2021
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Geometry-based distance decomposition for monocular 3d object detection
Xuepeng Shi, Qi Ye, Xiaozhi Chen, Chuangrong Chen, Zhixiang Chen, and Tae-Kyun Kim · 2021
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Depth-conditioned dynamic message propagation for monocular 3d object detection
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Progressive coordinate transforms for monocular 3d object detection
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Objects are different: Flexible monocular 3d object detection
Yunpeng Zhang, Jiwen Lu, and Jie Zhou · 2021
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Monocular 3d object detection: An extrinsic parameter free approach
Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, and Qinhong Jiang · 2021
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