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Most existing point cloud based 3D object detectors focus on the tasks of classification and box regression.
Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “STD: Sparse-to-dense 3D object detector for point cloud,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , Oct. 2019, pp. 1951–1960
1960
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition , Jun. 2012, pp. 3354–3361
2012
Earlier work this paper cites.
R. Girshick, “Fast R-CNN,” in 2015 IEEE International Conference on Computer Vision (ICCV) , Dec. 2015, pp. 1440–1448
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, no. 6, pp. 1137–1149, Jun. 2017
2017
Earlier work this paper cites.
S. Tang, Y. Li, L. Deng, and Y. Zhang, “Object localization based on proposal fusion,” IEEE Transactions on Multimedia , vol. 19, no. 9, pp. 2105–2116, Sep. 2017
2017
Earlier work this paper cites.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3D object detection network for autonomous driving,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jul. 2017, pp. 6526–6534
2017
Earlier work this paper cites.
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, “PointNet: Deep learning on point sets for 3D classification and segmentation,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jul. 2017, pp. 77–85
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “PointNet++: Deep hierarchical feature learning on point sets in a metric space,” in Proc. Adv. neural inf. proces. syst , Dec. 2017, pp. 5099–5108
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , Dec. 2017, pp. 5998–6008
2017
Earlier work this paper cites.
L. Tian, M. Li, Y. Hao, J. Liu, G. Zhang, and Y. Q. Chen, “Robust 3-D human detection in complex environments with a depth camera,” IEEE Transactions on Multimedia , vol. 20, no. 9, pp. 2249–2261, Sep. 2018
2018
Earlier work this paper cites.
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3D proposal generation and object detection from view aggregation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2018, pp. 1–8
2018
Earlier work this paper cites.
B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3D object detection from point clouds,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun. 2018, pp. 7652–7660
2018
Earlier work this paper cites.
B. Yang, M. Liang, and R. Urtasun, “HDNET: Exploiting HD maps for 3d object detection,” in Conference on Robot Learning (CoRL) , Dec. 2018, pp. 146–155
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “VoxelNet: End-to-end learning for point cloud based 3D object detection,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun. 2018, pp. 4490–4499
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “SECOND: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, pp. 3337–3354, Oct. 2018
2018
Earlier work this paper cites.
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum PointNets for 3D object detection from RGB-D data,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun. 2018, pp. 918–927
2018
Cited alongside, same era.
B. Jiang, R. Luo, J. Mao, T. Xiao, and Y. Jiang, “Acquisition of localization confidence for accurate object detection,” in Proceedings of the European Conference on Computer Vision (ECCV) , Sep. 2018, pp. 784–799
2018
Cited alongside, same era.
M. Liang, B. Yang, S. Wang, and R. Urtasun, “Deep continuous fusion for multi-sensor 3D object detection,” in Proceedings of the European Conference on Computer Vision (ECCV) , Sep. 2018, pp. 641–656
2018
Cited alongside, same era.
B. Graham, M. Engelcke, and L. v. d. Maaten, “3D semantic segmentation with submanifold sparse convolutional networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun. 2018, pp. 9224–9232
2018
Cited alongside, same era.
2019
Later among the works it cites.
V. A. Sindagi, Y. Zhou, and O. Tuzel, “MVX-Net: Multimodal VoxelNet for 3D object detection,” in 2019 International Conference on Robotics and Automation (ICRA) , May. 2019, pp. 7276–7282
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Wang and K. Jia, “Frustum ConvNet: Sliding frustums to aggregate local point-wise features for amodal,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Nov. 2019, pp. 1742–1749
2019
Later among the works it cites.
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W. Du, Y. Wang, and Y. Qiao, “Recurrent spatial-temporal attention network for action recognition in videos,” IEEE Transactions on Image Processing , vol. 27, no. 3, pp. 1347–1360, Mar. 2018
2018
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Cited alongside, same era.
S. Woo, J. Park, J. Lee, and I. S. Kweon, “CBAM: Convolutional block attention module,” in Proceedings of European Conference on Computer Vision , 2018, pp. 3–19
2018
Cited alongside, same era.
G. Cheng, J. Han, P. Zhou, and D. Xu, “Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection,” IEEE Transactions on Image Processing , vol. 28, no. 1, pp. 265–278, Jan. 2019
2019
Cited alongside, same era.
F. Sun, T. Kong, W. Huang, C. Tan, B. Fang, and H. Liu, “Feature pyramid reconfiguration with consistent loss for object detection,” IEEE Transactions on Image Processing , vol. 28, no. 10, pp. 5041–5051, Oct. 2019
2019
Cited alongside, same era.
G. Li, Y. Gan, H. Wu, N. Xiao, and L. Lin, “Cross-modal attentional context learning for RGB-D object detection,” IEEE Transactions on Image Processing , vol. 28, no. 4, pp. 1591–1601, Apr. 2019
2019
Cited alongside, same era.
X. Song, S. Jiang, L. Herranz, and C. Chen, “Learning effective RGB-D representations for scene recognition,” IEEE Transactions on Image Processing , vol. 28, no. 2, pp. 980–993, Feb. 2019
2019
Cited alongside, same era.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “PointPillars: Fast encoders for object detection from point clouds,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun. 2019, pp. 12 689–12 697
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
F. Huang, X. Zhang, Z. Zhao, and Z. Li, “Bi-directional spatial-semantic attention networks for image-text matching,” IEEE Transactions on Image Processing , vol. 28, no. 4, pp. 2008–2020, Apr. 2019
2019
Later among the works it cites.
Z. Zhang, Q. Wu, Y. Wang, and F. Chen, “High-quality image captioning with fine-grained and semantic-guided visual attention,” IEEE Transactions on Multimedia , vol. 21, no. 7, pp. 1681–1693, Jul. 2019
2019
Later among the works it cites.
Z. Fan, X. Zhao, T. Lin, and H. Su, “Attention-based multiview re-observation fusion network for skeletal action recognition,” IEEE Transactions on Multimedia , vol. 21, no. 2, pp. 363–374, Feb. 2019
2019
Later among the works it cites.
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai, and R. Yang, “IoU loss for 2D/3D object detection,” in 2019 International Conference on 3D Vision (3DV) , Sep. 2019, pp. 85–94
2019
Later among the works it cites.
Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9775–9784
2019
Later among the works it cites.
KITTI, “KITTI leader board of 3D object detection benchmark,” Mar. 2020. [Online]. Available: http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d
2020
Closest in time.
S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–1, 2020
2020
Closest in time.
H. Kuang, B. Wang, J. An, M. Zhang, and Z. Zhang, “Voxel-FPN: Multiscale voxel feature aggregation in 3D object detection from point clouds,” Sensors , vol. 20, pp. 704–721, Jan. 2020
2020
Closest in time.