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LiDAR is an important method for autonomous driving systems to sense the environment.
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 · 1903
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Deformable filter convolution for point cloud reasoning
Y. Xiong, M. Ren, R. Liao, K. Wong, and R. Urtasun · 1907
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Class-balanced grouping and sampling for point cloud 3d object detection
B. Zhu, Z. Jiang, X. Zhou, Z. Li, and G. Yu · 1908
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Deformable kernels: Adapting effective receptive fields for object deformation
H. Gao, X. Zhu, S. Lin, and J. Dai · 1910
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Lidar-based 3d object perception
M. Himmelsbach, A. Müller, T. Lüttel, and H.-J. Wünsche · 2008
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A perception-driven autonomous urban vehicle
J. J. Leonard, J. How, S. J. Teller, and M. Berger · 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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Holistic scene understanding for 3d object detection with rgbd cameras
D. Lin, S. Fidler, and R. Urtasun · 2013
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Sliding shapes for 3d object detection in depth images
S. Song and J. Xiao · 2014
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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
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ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 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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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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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
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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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Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 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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O-cnn: Octree-based convolutional neural networks
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong · 2017
Cited alongside, same era.
Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
Cited alongside, same era.
Pointcnn: Convolution on x x -transformed points
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
Cited alongside, same era.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 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. Waslander · 2018
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 · 2018
Cited alongside, same era.
Stereo r-cnn based 3d object detection for autonomous driving
P. Li, X. Chen, and S. Shen · 2019
Later among the works it cites.
Pointpillars: Fast encoders for object detection from point clouds
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom · 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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Fast point r-cnn
Y. Chen, S. Liu, X. Shen, and J. Jia · 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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Kpconv: Flexible and deformable convolution for point clouds
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas · 2019
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Pixor: Real-time 3d object detection from point clouds
B. Yang, W. Luo, and R. Urtasun · 2018
Cited alongside, same era.
Complex-yolo: Real-time 3d object detection on point clouds
M. Simon, S. Milz, K. Amende, and H.-M. Gross · 2018
Cited alongside, same era.
Second: Sparsely embedded convolutional detection
Y. Yan, Y. Mao, and B. Li · 2018
Cited alongside, same era.
Relation-shape convolutional neural network for point cloud analysis
Y. Liu, B. Fan, S. Xiang, and C. Pan · 2019
Cited alongside, same era.
Interpolated convolutional networks for 3d point cloud understanding
J. Mao, X. Wang, and H. Li · 2019
Cited alongside, same era.
Pointrcnn: 3d object proposal generation and detection from point cloud
S. Shi, X. Wang, and H. Li · 2019
Cited alongside, same era.
Later among the works it cites.
Disentangling monocular 3d object detection
A. Simonelli, S. R. Bulò, L. Porzi, M. López-Antequera, and P. Kontschieder · 2019
Later among the works it cites.
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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S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li · 2019
Later among the works it cites.
Ssn: Shape signature networks for multi-classobject detection from point clouds
X. Zhu, Y. Ma, T. Wang, Y. Xu, J. Shi, and D. Lin · 2020
Closest in time.
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
Closest in time.
Hvnet: Hybrid voxel network for lidar based 3d object detection
M. Ye, S. Xu, and T. Cao · 2020
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What you see is what you get: Exploiting visibility for 3d object detection
P. Hu, J. Ziglar, D. Held, and D. Ramanan · 2020
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Sarpnet: Shape attention regional proposal network for lidar-based 3d object detection
Y. Ye, H. Chen, C. Zhang, X. Hao, and Z. Zhang · 2020
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3dssd: Point-based 3d single stage object detector
Z. Yang, Y. Sun, S. Liu, and Jia · 2020
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Pointpainting: Sequential fusion for 3d object detection
S. Vora, A. H. Lang, B. Helou, and O. Beijbom · 2020
Closest in time.