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In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Sliding shapes for 3d object detection in depth images
S. Song and J. Xiao · 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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Fast r-cnn
R. Girshick · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 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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Sun rgb-d: A rgb-d scene understanding benchmark suite
S. Song, S. P. Lichtenberg, and J. Xiao · 2015
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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller · 2015
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Voting for voting in online point cloud object detection
D. Z. Wang and I. Posner · 2015
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3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
Data-driven 3d voxel patterns for object category recognition
Y. Xiang, W. Choi, Y. Lin, and S. Savarese · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
Cited alongside, same era.
Monocular 3d object detection for autonomous driving
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun · 2016
Cited alongside, same era.
3d fully convolutional network for vehicle detection in point cloud
B. Li · 2016
Cited alongside, same era.
Vehicle detection from 3d lidar using fully convolutional network
B. Li, T. Zhang, and T. Xia · 2016
Cited alongside, same era.
http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=bev
Kitti bird’s eye view object detection benchmark leader board · 2017
Closest in time.
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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Amodal detection of 3d objects: Inferring 3d bounding boxes from 2d ones in rgb-depth images
Z. Deng and L. J. Latecki · 2017
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Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks
M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner · 2017
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Dssd: Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
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Fpnn: Field probing neural networks for 3d data
Y. Li, S. Pirk, H. Su, C. R. Qi, and L. J. Guibas · 2016
Cited alongside, same era.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2016
Cited alongside, same era.
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.
3d bounding box estimation using deep learning and geometry
A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka · 2016
Cited alongside, same era.
Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas · 2016
Cited alongside, same era.
Three-dimensional object detection and layout prediction using clouds of oriented gradients
Z. Ren and E. B. Sudderth · 2016
Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoys, and A. Geiger · 2016
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Closest in time.
2d-driven 3d object detection in rgb-d images
J. Lahoud and B. Ghanem · 2017
Closest in time.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Closest in time.
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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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, 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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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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O-cnn: Octree-based convolutional neural networks for 3d shape analysis
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong · 2017
Closest in time.
Vehicle detection and localization on bird’s eye view elevation images using convolutional neural network
S.-L. Yu, T. Westfechtel, R. Hamada, K. Ohno, and S. Tadokoro · 2017
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