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Depth perception is a crucial component of monoc-ular 3D detection tasks that typically involve ill-posed problems.
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H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. (CVPR)
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S. Wu, X. Li, and X. Wang, “Iou-aware single-stage object detector for accurate localization,” Image Vis. Comput
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Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan, and W. Ouyang, “Geometry uncertainty projection network for monocular 3d object detection,” in Int. Conf. Comput. Vis. (ICCV)
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X. Li, W. Wang, L. Wu, S. Chen, X. Hu, J. Li, J. Tang, and J. Yang, “Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,” in Adv. Neural Inform. Process. Syst. (NeurIPS)
2020
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S. Wu, X. Li, and X. Wang, “Iou-aware single-stage object detector for accurate localization,” Image Vis. Comput
2020
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M. Contributors, “MMDetection3D: OpenMMLab next-generation platform for general 3D object detection.” https://github.com/open-mmlab/mmdetection3d , 2020
2020
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T. Wang, X. Zhu, J. Pang, and D. Lin, “FCOS3D: fully convolutional one-stage monocular 3d object detection,” in Int. Conf. Comput. Vis. Worksh. (ICCV Workshop)
2021
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T. Wang, X. Zhu, J. Pang, and D. Lin, “Probabilistic and geometric depth: Detecting objects in perspective,” in Annu. Conf. Robot. Learn. (CoRL)
2021
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C. Reading, A. Harakeh, J. Chae, and S. L. Waslander, “Categorical depth distribution network for monocular 3d object detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. (CVPR)
2021
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D. Park, R. Ambrus, V. Guizilini, J. Li, and A. Gaidon, “Is pseudo-lidar needed for monocular 3d object detection?,” in Int. Conf. Comput. Vis. (ICCV)
2021
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2021
Cited alongside, same era.
2022
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J. Yang, S. Liu, Z. Li, X. Li, and J. Sun, “Real-time object detection for streaming perception,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022
2022
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J. Yang, S. Liu, Z. Li, X. Li, and J. Sun, “Streamyolo: Real-time object detection for streaming perception,” CoRR
2022
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J. Huang and G. Huang, “Bevdet4d: Exploit temporal cues in multi-camera 3d object detection,” CoRR
2022
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2022
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J. Yang, S. Wu, L. Gou, H. Yu, C. Lin, J. Wang, P. Wang, M. Li, and X. Li, “SCD: A stacked carton dataset for detection and segmentation,” Sensors
2022
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Z. Liu, H. Mao, C. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. (CVPR)
2022
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X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, and C. Tai, “Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. (CVPR)
2022
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Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” 2023
2023
Closest in time.
Y. Li, Z. Ge, G. Yu, J. Yang, Z. Wang, Y. Shi, J. Sun, and Z. Li, “Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,” in AAAI Conf. Artif. Intell. (AAAI)
2023
Closest in time.