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In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection.
Zhou, X.; Wang, D.; and Krähenbühl, P. 2019 · 1904
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Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
You, Y.; Wang, Y.; Chao, W.-L.; Garg, D.; Pleiss, G.; Hariharan, B.; Campbell, M.; and Weinberger, K. Q. 2019 · 1906
Earlier work this paper cites.
Class-balanced grouping and sampling for point cloud 3d object detection
Zhu, B.; Jiang, Z.; Zhou, X.; Li, Z.; and Yu, G. 2019 · 1908
Earlier work this paper cites.
Deep ordinal regression network for monocular depth estimation
Fu, H.; Gong, M.; Wang, C.; Batmanghelich, K.; and Tao, D. 2018 · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D.; Puhrsch, C.; and Fergus, R. 2014 · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Unsupervised monocular depth estimation with left-right consistency
Godard, C.; Mac Aodha, O.; and Brostow, G. J. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
Earlier work this paper cites.
Squeeze-and-excitation networks
Hu, J.; Shen, L.; and Sun, G. 2018 · 2018
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Orthographic feature transform for monocular 3d object detection
Roddick, T.; Kendall, A.; and Cipolla, R. 2018 · 2018
Earlier work this paper cites.
Second: Sparsely embedded convolutional detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
Earlier work this paper cites.
Mvsnet: Depth inference for unstructured multi-view stereo
Yao, Y.; Luo, Z.; Li, S.; Fang, T.; and Quan, L. 2018 · 2018
Earlier work this paper cites.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Zhou, Y.; and Tuzel, O. 2018 · 2018
Cited alongside, same era.
M3d-rpn: Monocular 3d region proposal network for object detection
Brazil, G.; and Liu, X. 2019 · 2019
Cited alongside, same era.
Digging into self-supervised monocular depth estimation
Godard, C.; Mac Aodha, O.; Firman, M.; and Brostow, G. J. 2019 · 2019
Cited alongside, same era.
Pointpillars: Fast encoders for object detection from point clouds
Lang, A. H.; Vora, S.; Caesar, H.; Zhou, L.; Yang, J.; and Beijbom, O. 2019 · 2019
Cited alongside, same era.
Deep hough voting for 3d object detection in point clouds
Qi, C. R.; Litany, O.; He, K.; and Guibas, L. J. 2019 · 2019
Cited alongside, same era.
Pointrcnn: 3d object proposal generation and detection from point cloud
Shi, S.; Wang, X.; and Li, H. 2019 · 2019
Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d
Philion, J.; and Fidler, S. 2020 · 2020
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End-to-end pseudo-lidar for image-based 3d object detection
Qian, R.; Garg, D.; Wang, Y.; You, Y.; Belongie, S.; Hariharan, B.; Campbell, M.; Weinberger, K. Q.; and Chao, W.-L. 2020 · 2020
Later among the works it cites.
Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
Shi, S.; Guo, C.; Jiang, L.; Wang, Z.; Shi, J.; Wang, X.; and Li, H. 2020 · 2020
Later among the works it cites.
Scalability in perception for autonomous driving: Waymo open dataset
Sun, P.; Kretzschmar, H.; Dotiwalla, X.; Chouard, A.; Patnaik, V.; Tsui, P.; Guo, J.; Zhou, Y.; Chai, Y.; Caine, B.; et al. 2020 · 2020
Later among the works it cites.
Adabins: Depth estimation using adaptive bins
Bhat, S. F.; Alhashim, I.; and Wonka, P. 2021 · 2021
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Cited alongside, same era.
Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Wang, Y.; Chao, W.-L.; Garg, D.; Hariharan, B.; Campbell, M.; and Weinberger, K. Q. 2019 · 2019
Cited alongside, same era.
Mvscrf: Learning multi-view stereo with conditional random fields
Xue, Y.; Chen, J.; Wan, W.; Huang, Y.; Yu, C.; Li, T.; and Bao, J. 2019 · 2019
Cited alongside, same era.
Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao, Y.; Luo, Z.; Li, S.; Shen, T.; Fang, T.; and Quan, L. 2019 · 2019
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
Caesar, H.; Bankiti, V.; Lang, A. H.; Vora, S.; Liong, V. E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; and Beijbom, O. 2020 · 2020
Cited alongside, same era.
End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
Cited alongside, same era.
Cascade cost volume for high-resolution multi-view stereo and stereo matching
Gu, X.; Fan, Z.; Zhu, S.; Dai, Z.; Tan, F.; and Tan, P. 2020 · 2020
Cited alongside, same era.
Huang, J.; Huang, G.; Zhu, Z.; and Du, D. 2021 · 2021
Later among the works it cites.
Categorical depth distribution network for monocular 3d object detection
Reading, C.; Harakeh, A.; Chae, J.; and Waslander, S. L. 2021 · 2021
Later among the works it cites.
Center-based 3d object detection and tracking
Yin, T.; Zhou, X.; and Krahenbuhl, P. 2021 · 2021
Later among the works it cites.
Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
Bae, G.; Budvytis, I.; and Cipolla, R. 2022 · 2022
Closest in time.
BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection
Huang, J.; and Huang, G. 2022 · 2022
Closest in time.
Imvoxelnet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection
Rukhovich, D.; Vorontsova, A.; and Konushin, A. 2022 · 2022
Closest in time.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Yang, J.; Song, L.; Liu, S.; Li, Z.; Li, X.; Sun, H.; Sun, J.; and Zheng, N. 2022 · 2022
Closest in time.