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The objective of the collaborative vehicle-to-everything perception task is to enhance the individual vehicle's perception capability through message communication among neighboring traffic agents.
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2020
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X. Chen, B. Yan, J. Zhu, D. Wang, X. Yang, and H. Lu, “Transformer tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 8126–8135
2021
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2021
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Y. Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning distilled collaboration graph for multi-agent perception,” Advances in Neural Information Processing Systems , vol. 34, pp. 29 541–29 552, 2021
2021
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2021
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2021
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Z. Li, F. Wang, and N. Wang, “Lidar r-cnn: An efficient and universal 3d object detector,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7546–7555
2021
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Z. Bai, G. Wu, M. J. Barth, Y. Liu, E. A. Sisbot, and K. Oguchi, “Pillargrid: Deep learning-based cooperative perception for 3d object detection from onboard-roadside lidar,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 1743–1749
2022
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Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan, and Y. Guo, “Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 953–18 962
2022
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J. S. Hu, T. Kuai, and S. L. Waslander, “Point density-aware voxels for lidar 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8469–8478
2022
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C. Pan, Y. He, J. Peng, Q. Zhang, W. Sui, and Z. Zhang, “Baeformer: Bi-directional and early interaction transformers for bird’s eye view semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9590–9599
2023
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R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song et al. , “V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 712–13 722
2023
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R. Xu, Z. Tu, H. Xiang, W. Shao, B. Zhou, and J. Ma, “Cobevt: Cooperative bird’s eye view semantic segmentation with sparse transformers,” in Conference on Robot Learning . PMLR, 2023, pp. 989–1000
2023
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2023
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Y. Lu, Q. Li, B. Liu, M. Dianati, C. Feng, S. Chen, and Y. Wang, “Robust collaborative 3d object detection in presence of pose errors,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4812–4818
2023
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2023
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2023
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2023
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D. Qiao and F. Zulkernine, “Adaptive feature fusion for cooperative perception using lidar point clouds,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 1186–1195
2023
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Y. Chen, J. Liu, X. Zhang, X. Qi, and J. Jia, “Voxelnext: Fully sparse voxelnet for 3d object detection and tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 674–21 683
2023
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Z. Liu, X. Yang, H. Tang, S. Yang, and S. Han, “Flatformer: Flattened window attention for efficient point cloud transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1200–1211
2023
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H. Yu, W. Yang, H. Ruan, Z. Yang, Y. Tang, X. Gao, X. Hao, Y. Shi, Y. Pan, N. Sun et al. , “V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5486–5495
2023
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S. Wei, Y. Wei, Y. Hu, Y. Lu, Y. Zhong, S. Chen, and Y. Zhang, “Asynchrony-robust collaborative perception via bird’s eye view flow,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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