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Three-dimensional object detection from a single view is a challenging task which, if performed with good accuracy, is an important enabler of low-cost mobile robot perception.
Are we ready for autonomous driving? The KITTI vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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3D object proposals for accurate object class detection
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler, and R. Urtasun · 2015
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The Pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
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Matrix backpropagation for deep networks with structured layers
C. Ionescu, O. Vantzos, and C. Sminchisescu · 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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Data-driven 3D voxel patterns for object category recognition
Y. Xiang, W. Choi, Y. Lin, and S. Savarese · 2015
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The fast bilateral solver
J. T. Barron and B. Poole · 2016
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Pose-RCNN: Joint object detection and pose estimation using 3D object proposals
M. Braun, Q. Rao, Y. Wang, and F. Flohr · 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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Vehicle detection and road scene segmentation using deep learning
A. Krishnan and J. Larsson · 2016
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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
Cited alongside, same era.
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. Teuliere, and T. Chateau · 2017
Cited alongside, same era.
Multi-view 3D object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
Cited alongside, same era.
Joint 3D proposal generation and object detection from view aggregation
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
Later among the works it cites.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
A. Kendall, Y. Gal, and R. Cipolla · 2018
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3d-rcnn: Instance-level 3d object reconstruction via render-and-compare
A. Kundu, Y. Li, and J. M. Rehg · 2018
Later among the works it cites.
Deep continuous fusion for multi-sensor 3D object detection
M. Liang, B. Yang, S. Wang, and R. Urtasun · 2018
Later among the works it cites.
Frustum pointnets for 3D object detection from RGB-D data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2018
Later among the works it cites.
Real-time seamless single shot 6D object pose prediction
B. Tekin, S. N. Sinha, and P. Fua · 2018
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T.-Y. Lin, P. Dollar, 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.
3D bounding box estimation using deep learning and geometry
A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka · 2017
Cited alongside, same era.
BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth
M. Rad and V. Lepetit · 2017
Cited alongside, same era.
YOLO9000: Better, faster, stronger
J. Redmon and A. Farhadi · 2017
Cited alongside, same era.
SqueezeDet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving
B. Wu, F. Iandola, P. H. Jin, and K. Keutzer · 2017
Cited alongside, same era.
Subcategory-aware convolutional neural networks for object proposals and detection
Y. Xiang, W. Choi, Y. Lin, and S. Savarese · 2017
Cited alongside, same era.
Multi-level fusion based 3D object detection from monocular images
B. Xu and Z. Chen · 2018
Later among the works it cites.
PointFusion: Deep sensor fusion for 3D bounding box estimation
D. Xu, D. Anguelov, and A. Jain · 2018
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Second: Sparsely embedded convolutional detection
Y. Yan, Y. Mao, and B. Li · 2018
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PIXOR: Real-time 3D object detection from point clouds
B. Yang, W. Luo, and R. Urtasun · 2018
Later among the works it cites.
Voxelnet: End-to-end learning for point cloud based 3D object detection
Y. Zhou and O. Tuzel · 2018
Later among the works it cites.
MonoGRNet: A geometric reasoning network for 3D object localization
Z. Qin, J. Wang, and Y. Lu · 2019
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