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Accurate multi-view 3D object detection is essential for applications such as autonomous driving.
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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 Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3142–3152
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J. Li, C. Luo, and X. Yang, “Pillarnext: Rethinking network designs for 3d object detection in lidar point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 567–17 576
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2021
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X. Dai, Z. Jiang, Z. Wu, Y. Bao, Z. Wang, S. Liu, and E. Zhou, “General instance distillation for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7842–7851
2021
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X. Song, D. Zhou, W. Li, H. Ding, Y. Dai, and L. Zhang, “Wsamf-net: Wavelet spatial attention-based multistream feedback network for single image dehazing,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 2, pp. 575–588, 2022
2022
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2022
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2022
Cited alongside, same era.
Y. Hong, H. Dai, and Y. Ding, “Cross-modality knowledge distillation network for monocular 3d object detection,” in European Conference on Computer Vision . Springer, 2022, pp. 87–104
2022
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2022
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2022
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L. Yang, X. Zhang, J. Li, L. Wang, M. Zhu, C. Zhang, and H. Liu, “Mix-teaching: A simple, unified and effective semi-supervised learning framework for monocular 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 11, pp. 6832–6844, 2023
2023
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C. Tao, J. Cao, C. Wang, Z. Zhang, and Z. Gao, “Pseudo-mono for monocular 3d object detection in autonomous driving,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 8, pp. 3962–3975, 2023
2023
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C. Yu, B. Peng, Q. Huang, and J. Lei, “Pipc-3ddet: Harnessing perspective information and proposal correlation for 3d point cloud object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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Z. Song, C. Jia, L. Yang, H. Wei, and L. Liu, “Graphalign++: An accurate feature alignment by graph matching for multi-modal 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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S. Wang, Y. Liu, T. Wang, Y. Li, and X. Zhang, “Exploring object-centric temporal modeling for efficient multi-view 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3621–3631
2023
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2023
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S. Zhou, W. Liu, C. Hu, S. Zhou, and C. Ma, “Unidistill: A universal cross-modality knowledge distillation framework for 3d object detection in bird’s-eye view,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5116–5125, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:257766394
2023
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M. Klingner, S. Borse, V. R. Kumar, B. Rezaei, V. Narayanan, S. Yogamani, and F. Porikli, “X3kd: Knowledge distillation across modalities, tasks and stages for multi-camera 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 343–13 353
2023
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Z. Wang, D. Li, C. Luo, C. Xie, and X. Yang, “Distillbev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8637–8646
2023
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2023
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J. Zeng, L. Chen, H. Deng, L. Lu, J. Yan, Y. Qiao, and H. Li, “Distilling focal knowledge from imperfect expert for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 992–1001
2023
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Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” in 2023 IEEE international conference on robotics and automation (ICRA) . IEEE, 2023, pp. 2774–2781
2023
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2024
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2024
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X. Jiang, S. Li, Y. Liu, S. Wang, F. Jia, T. Wang, L. Han, and X. Zhang, “Far3d: Expanding the horizon for surround-view 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 3, 2024, pp. 2561–2569
2024
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2024
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S. Xu, F. Li, Z. Song, J. Fang, S. Wang, and Z.-X. Yang, “Multi-sem fusion: multimodal semantic fusion for 3d object detection,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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2024
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H. Zhao, Q. Zhang, S. Zhao, Z. Chen, J. Zhang, and D. Tao, “Simdistill: Simulated multi-modal distillation for bev 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 7, 2024, pp. 7460–7468
2024
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S. Jang, D. U. Jo, S. J. Hwang, D. Lee, and D. Ji, “Stxd: structural and temporal cross-modal distillation for multi-view 3d object detection,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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