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Monocular 3D object detection, with the aim of predicting the geometric properties of on-road objects, is a promising research topic for the intelligent perception systems of autonomous driving.
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2017
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2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
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J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7263–7271
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2018
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2018
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2018
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2018
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2018
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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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Z. Rozsa and T. Sziranyi, “Object detection from a few lidar scanning planes,” IEEE Transactions on Intelligent Vehicles , vol. 4, no. 4, pp. 548–560, 2019
2019
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X. Lu, B. Li, Y. Yue, Q. Li, and J. Yan, “Grid r-cnn,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7363–7372
2019
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Z. Qin, J. Wang, and Y. Lu, “Monogrnet: A geometric reasoning network for monocular 3d object localization,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 8851–8858
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X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9308–9316
2019
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems , 2019, pp. 8026–8037
2019
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Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 040–11 048
2020
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Z. Liu, Z. Wu, and R. Tóth, “Smoke: Single-stage monocular 3d object detection via keypoint estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 996–997
2020
Closest in time.
Y. Chen, L. Tai, K. Sun, and M. Li, “Monopair: Monocular 3d object detection using pairwise spatial relationships,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 093–12 102
2020
Closest in time.
P. Li, H. Zhao, P. Liu, and F. Cao, “Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving,” in Proceedings of the European conference on computer vision (ECCV) , 2020, pp. 644–660
2020
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2020
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F. Chabot, M. Chaouch, J. Rabarisoa, C. Teuliere, and T. Chateau, “Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2040–2049
2049
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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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2069–2078
2078
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