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We present a novel 3D object detection framework, named IPOD, based on raw point cloud.
Multiple 3d object tracking for augmented reality
Y. Park, V. Lepetit, and W. Woo · 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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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. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Pedestrian detection combining RGB and dense LIDAR data
C. Premebida, J. Carreira, J. Batista, and U. Nunes · 2014
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Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection
A. González, G. Villalonga, J. Xu, D. Vázquez, J. Amores, and A. M. López · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 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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Voting for voting in online point cloud object detection
D. Z. Wang and I. Posner · 2015
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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, S. Ghemawat, I. J. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Józefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. G. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. A. Tucker, V. Vanhoucke, V. Vasudevan, F. B. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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SSD: single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C. Berg · 2016
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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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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. A. Funkhouser, and M. Nießner · 2017
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Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 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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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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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2017
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http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d , 2018
”kitti 3d object detection benchmark” · 2018
Closest in time.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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DSSD : Deconvolutional single shot detector
C. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 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.
3d fully convolutional network for vehicle detection in point cloud
B. Li · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
Frustum pointnets for 3d object detection from RGB-D data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Closest in time.
3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
A. Dai and M. Nießner · 2018
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Pointsift: A sift-like network module for 3d point cloud semantic segmentation
M. Jiang, Y. Wu, and C. Lu · 2018
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So-net: Self-organizing network for point cloud analysis
J. Li, B. M. Chen, and G. H. Lee · 2018
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Pointcnn
Y. Li, R. Bu, M. Sun, and B. Chen · 2018
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Squeezeseg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3d lidar point cloud
B. Wu, A. Wan, X. Yue, and K. Keutzer · 2018
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