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This paper presents a novel 3D object detection framework that processes LiDAR data directly on its native representation: range images.
Separate visual pathways for perception and action
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Advantages of exploiting projection structure for segmenting dense 3d point clouds
A. Bewley and B. Upcroft · 2013
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Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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Voting for voting in online point cloud object detection
D. Z. Wang and I. Posner · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 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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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data
L. Beyer, A. Hermans, and B. Leibe · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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J. L. Ba, J. R. Kiros, and G. E. Hinton · 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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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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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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Learning dilation factors for semantic segmentation of street scenes
Y. He, M. Keuper, B. Schiele, and M. Fritz · 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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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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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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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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Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection
Z. Wang and K. Jia · 2019
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Pointrcnn: 3d object proposal generation and detection from point cloud
S. Shi, X. Wang, and H. Li · 2019
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Std: Sparse-to-dense 3d object detector for point cloud
Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia · 2019
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3d neighborhood convolution: Learning depth-aware features for rgb-d and rgb semantic segmentation
Y. Chen, T. Mensink, and E. Gavves · 2019
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Deformable convnets v2: More deformable, better results
X. Zhu, H. Hu, S. Lin, and J. Dai · 2019
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Part-aˆ 2 net: 3d part-aware and aggregation neural network for object detection from point cloud
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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
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 · 2018
Cited alongside, same era.
Pixor: Real-time 3d object detection from point clouds
B. Yang, W. Luo, and R. Urtasun · 2018
Cited alongside, same era.
Complex-yolo: An euler-region-proposal for real-time 3d object detection on point clouds
M. Simony, S. Milzy, K. Amendey, and H.-M. Gross · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
B. Graham, M. Engelcke, and L. van der Maaten · 2018
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Depth-aware cnn for rgb-d segmentation
W. Wang and U. Neumann · 2018
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Visual reasoning with multi-hop feature modulation
F. Strub, M. Seurin, E. Perez, H. De Vries, J. Mary, P. Preux, and A. CourvilleOlivier Pietquin · 2018
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S. Shi, Z. Wang, X. Wang, and H. Li · 2019
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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 · 2019
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Rangenet++: Fast and accurate lidar semantic segmentation
A. Milioto, I. Vizzo, J. Behley, and C. Stachniss · 2019
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End-to-end multi-view fusion for 3d object detection in lidar point clouds
Y. Zhou, P. Sun, Y. Zhang, D. Anguelov, J. Gao, T. Ouyang, J. Guo, J. Ngiam, and V. Vasudevan · 2019
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Starnet: Targeted computation for object detection in point clouds
J. Ngiam, B. Caine, W. Han, B. Yang, Y. Chai, P. Sun, Y. Zhou, X. Yi, O. Alsharif, P. Nguyen, et al · 2019
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Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
F. Engelmann, T. Kontogianni, and B. Leibe · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al · 2020
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Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li · 2020
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Structure aware single-stage 3d object detection from point cloud
C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang · 2020
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Learning depth-guided convolutions for monocular 3d object detection
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu, and P. Luo · 2020
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Origins and mitigations of some automotive pulsed lidar artifacts
M. A. Shand · 2020
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