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3D object detection is an important capability needed in various practical applications such as driver assistance systems.
nuscenes: A multimodal dataset for autonomous driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom · 1903
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X. Zhou, D. Wang, and P. Krähenbühl · 1904
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Monocular 3d object detection and box fitting trained end-to-end using intersection-over-union loss
E. Jörgensen, C. Zach, and F. Kahl · 1906
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Learning depth from single monocular images
A. Saxena, S. Chung, and A. Ng · 2005
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Center3d: Center-based monocular 3d object detection with joint depth understanding
Y. Tang, S. Dorn, and C. Savani · 2005
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Putting objects in perspective
D. Hoiem, A. A. Efros, and M. Hebert · 2006
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Make3d: Learning 3d scene structure from a single still image
A. Saxena, M. Sun, and A. Y. Ng · 2008
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Fast r-cnn
R. Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Densebox: Unifying landmark localization with end to end object detection
L. Huang, Y. Yang, Y. Deng, and Y. Yu · 2015
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3d object proposals for accurate object class detection
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun · 2015
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg, V. K. BG, and I. Reid · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Monocular 3d object detection for autonomous driving
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun · 2016
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Yolo9000: Better, faster, stronger
J. Redmon and A. Farhadi · 2017
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Unsupervised monocular depth estimation with leftright consistency
C. Godard, O. M. Aodha, and G. J. Brostow · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. Lowe · 2017
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3d bounding box estimation using deep learning and geometry
A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
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Disentangling monocular 3d object detection
A. Simonelli, S. R. R. Bulò, L. Porzi, M. López-Antequera, and P. Kontschieder · 2019
Later among the works it cites.
M3d-rpn: Monocular 3d region proposal network for object detection
G. Brazil and X. Liu · 2019
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Fcos: Fully convolutional one-stage object detection
Z. Tian, C. Shen, H. Chen, and T. He · 2019
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Monogrnet: A geometric reasoning network for monocular 3d object localization
Z. Qin, J. Wang, and Y. Lu · 2019
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Deep fitting degree scoring network for monocular 3d object detection
L. Liu, J. Lu, C. Xu, Q. Tian, and J. Zhou · 2019
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Gs3d: An efficient 3d object detection framework for autonomous driving
B. Li, W. Ouyang, L. Sheng, X. Zeng, and X. Wang · 2019
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Multi-level fusion based 3d object detection from monocular images
B. Xu and Z. Chen · 2018
Cited alongside, same era.
Cornernet: Detecting objects as paired keypoints
H. Law and J. Deng · 2018
Cited alongside, same era.
Deep ordinal regression network for monocular depth estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
Cited alongside, same era.
Learning monocular depth by distilling cross-domain stereo networks
X. Guo, H. Li, S. Yi, J. Ren, and X. Wang · 2018
Cited alongside, same era.
Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Z. Yin and J. Shi · 2018
Cited alongside, same era.
Deep ordinal regression network for monocular depth estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
Cited alongside, same era.
Later among the works it cites.
Reconfigurable voxels: A new representation for lidar-based point clouds
T. Wang, X. Zhu, and D. Lin · 2020
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Ssn: Shape signature networks for multi-class object detection from point clouds
X. Zhu, Y. Ma, T. Wang, Y. Xu, J. Shi, and D. Lin · 2020
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Centerfusion: Center-based radar and camera fusion for 3d object detection
R. Nabati and H. Qi · 2020
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Ida-3d: Instance-depth-aware 3d object detection from stereo vision for autonomous driving
W. Peng, H. Pan, H. Liu, and Y. Sun · 2020
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Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving
P. Li, H. Zhao, P. Liu, and F. Cao · 2020
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Task-aware monocular depth estimation for 3d object detection
X. Wang, W. Yin, T. Kong, Y. Jiang, L. Li, and C. Shen · 2020
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Monopair: Monocular 3d object detection using pairwise spatial relationships
Y. Chen, L. Tai, K. Sun, and M. Li · 2020
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MMDetection3D: OpenMMLab next-generation platform for general 3D object detection
M. Contributors · 2020
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Towards generalization across depth for monocular 3d object detection
A. Simonelli, S. R. Bulò, L. Porzi, E. Ricci, and P. Kontschieder · 2020
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Flava: Find, localize, adjust and verify to annotate lidar-based point clouds
T. Wang, C. He, Z. Wang, J. Shi, and D. Lin · 2020
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Cylindrical and asymmetrical 3d convolution networks for lidar segmentation
X. Zhu, H. Zhou, T. Wang, F. Hong, Y. Ma, W. Li, H. Li, and D. Lin · 2021
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FCOS3D: Fully convolutional one-stage monocular 3d object detection
T. Wang, X. Zhu, J. Pang, and D. Lin · 2021
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Center-based 3d object detection and tracking
T. Yin, X. Zhou, and P. Krähenbühl · 2021
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