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Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 580–587
2014
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P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Lecun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” in International Conference on Learning Representations , 2014
2014
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S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 567–576
2015
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R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
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K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 37, no. 9, pp. 1904–1916, 2015
2015
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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, 2016
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J. Dai, Y. Li, K. He, and J. Sun, “R-fcn: Object detection via region-based fully convolutional networks,” in Advances in neural information processing systems , 2016, pp. 379–387
2016
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision , 2016, pp. 21–37
2016
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S. Song and J. Xiao, “Deep sliding shapes for amodal 3d object detection in rgb-d images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 808–816
2016
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Z. Ren and E. B. Sudderth, “Three-dimensional object detection and layout prediction using clouds of oriented gradients,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1525–1533
2016
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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5828–5839
2017
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , vol. 1, no. 2, p. 4, 2017
2017
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in Neural Information Processing Systems , 2017, pp. 5099–5108
2017
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
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F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe, “Exploring spatial context for 3d semantic segmentation of point clouds,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 716–724
2017
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M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3693–3702
2017
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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 , 2017, pp. 5998–6008
2017
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A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap, “A simple neural network module for relational reasoning,” in Advances in neural information processing systems , 2017, pp. 4967–4976
2017
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J. Lahoud and B. Ghanem, “2d-driven 3d object detection in rgb-d images,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 4622–4630
2017
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M. Feng, Y. Wang, J. Liu, L. Zhang, H. F. Zaki, and A. Mian, “Benchmark data set and method for depth estimation from light field images,” IEEE Transactions on Image Processing , vol. 27, no. 7, pp. 3586–3598, 2018
2018
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M. Feng, S. Zulqarnain Gilani, Y. Wang, and A. Mian, “3d face reconstruction from light field images: A model-free approach,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 501–518
2018
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X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv preprint arXiv:1904.07850 , 2019
2019
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2019
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2019
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2019
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C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3d object detection in point clouds,” Proceedings of the IEEE Conference on Computer Vision , 2019
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H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei, “Relation networks for object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3588–3597
2018
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2018
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Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, and J. Sun, “Light-head r-cnn: In defense of two-stage object detector,” 2018
2018
Cited alongside, same era.
B. Wu, A. Wan, X. Yue, and K. Keutzer, “Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1887–1893
2018
Cited alongside, same era.
G. Yang, Y. Cui, S. Belongie, and B. Hariharan, “Learning single-view 3d reconstruction with limited pose supervision,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 86–101
2018
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Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4490–4499
2018
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B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d object detection from point clouds,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 7652–7660
2018
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C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” Proceedings of the IEEE Conference on Computer Vision , 2018
2018
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2019
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J. Wu, L. Wang, L. Wang, J. Guo, and G. Wu, “Learning actor relation graphs for group activity recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9964–9974
2019
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G. Wang, K. Wang, and L. Lin, “Adaptively connected neural networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
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W.-H. Li, F.-T. Hong, and W.-S. Zheng, “Learning to learn relation for important people detection in still images,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
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J. Choe and H. Shim, “Attention-based dropout layer for weakly supervised object localization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2219–2228
2019
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X. Zhou, J. Zhuo, and P. Krahenbuhl, “Bottom-up object detection by grouping extreme and center points,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 850–859
2019
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A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 697–12 705
2019
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S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 770–779
2019
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2019
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Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” ACM Transactions on Graphics , 2019
2019
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Y. Duan, Y. Zheng, J. Lu, J. Zhou, and Q. Tian, “Structural relational reasoning of point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 949–958
2019
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Y. Chen, M. Rohrbach, Z. Yan, Y. Shuicheng, J. Feng, and Y. Kalantidis, “Graph-based global reasoning networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 433–442
2019
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2019
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J. Hou, A. Dai, and M. Nießner, “3d-sis: 3d semantic instance segmentation of rgb-d scans,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4421–4430
2019
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Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “Std: Sparse-to-dense 3d object detector for point cloud,” Proceedings of the IEEE Conference on Computer Vision , 2019
2019
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