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3D object detection is an essential task in autonomous driving.
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Are we ready for autonomous driving? the kitti vision benchmark suite
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Depth extraction from video using non-parametric sampling
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Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Depth map prediction from a single image using a multi-scale deep network
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Microsoft coco: Common objects in context
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Efficient joint segmentation, occlusion labeling, stereo and flow estimation
K. Yamaguchi, D. McAllester, and R. Urtasun · 2014
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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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Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 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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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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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
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Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière, and T. Chateau · 2017
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Multi-view 3d object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
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Unsupervised monocular depth estimation with left-right consistency
C. Godard, O. Mac Aodha, and G. J. Brostow · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Multi-scale dense convolutional networks for efficient prediction
Pyramid stereo matching network
J.-R. Chang and Y.-S. Chen · 2018
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3d object proposals using stereo imagery for accurate object class detection
X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler, and R. Urtasun · 2018
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Deep ordinal regression network for monocular depth estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
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Joint 3d proposal generation and object detection from view aggregation
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander · 2018
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Deep continuous fusion for multi-sensor 3d object detection
M. Liang, B. Yang, S. Wang, and R. Urtasun · 2018
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Frustum pointnets for 3d object detection from rgb-d data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2018
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G. Huang, D. Chen, T. Li, F. Wu, L. van der Maaten, and K. Q. Weinberger · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Cited alongside, same era.
3d bounding box estimation using deep learning and geometry
A. Mousavian, D. Anguelov, J. Flynn, and J. Košecká · 2017
Cited alongside, same era.
Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks
C. C. Pham and J. W. Jeon · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Accurate single stage detector using recurrent rolling convolution
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y.-W. Tai, and L. Xu · 2017
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Subcategory-aware convolutional neural networks for object proposals and detection
Y. Xiang, W. Choi, Y. Lin, and S. Savarese · 2017
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Deep parametric continuous convolutional neural networks
S. Wang, S. Suo, W.-C. M. A. Pokrovsky, and R. Urtasun · 2018
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Anytime stereo image depth estimation on mobile devices
Y. Wang, Z. Lai, G. Huang, B. H. Wang, L. van der Maaten, M. Campbell, and K. Q. Weinberger · 2018
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Multi-level fusion based 3d object detection from monocular images
B. Xu and Z. Chen · 2018
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Pointfusion: Deep sensor fusion for 3d bounding box estimation
D. Xu, D. Anguelov, and A. Jain · 2018
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Pixor: Real-time 3d object detection from point clouds
B. Yang, W. Luo, and R. Urtasun · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
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