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3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
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X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun, “3d object proposals for accurate object class detection,” in Advances in Neural Information Processing Systems , 2015, pp. 424–432
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X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2147–2156
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2117–2125
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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,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 652–660
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2017
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2017
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M. Bai and R. Urtasun, “Deep watershed transform for instance segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5221–5229
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S. Sun, J. Pang, J. Shi, S. Yi, and W. Ouyang, “Fishnet: A versatile backbone for image, region, and pixel level prediction,” in Advances in Neural Information Processing Systems , 2018, pp. 762–772
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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,” CVPR , 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
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F. Manhardt, W. Kehl, and A. Gaidon, “Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape,” in Computer Vision and Pattern Recognition (CVPR) . IEEE, 2019
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P. Li, X. Chen, and S. Shen, “Stereo r-cnn based 3d object detection for autonomous driving,” in CVPR , 2019
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Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, and K. Weinberger, “Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving,” in CVPR , 2019
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L. Yi, W. Zhao, H. Wang, M. Sung, and L. J. Guibas, “Gspn: Generative shape proposal network for 3d instance segmentation in point cloud,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 3947–3956
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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
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X. Wang, S. Liu, X. Shen, C. Shen, and J. Jia, “Associatively segmenting instances and semantics in point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4096–4105
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2019
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H. Zhao, L. Jiang, C.-W. Fu, and J. Jia, “Pointweb: Enhancing local neighborhood features for point cloud processing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5565–5573
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W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9621–9630
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2049
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