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Camera-based Bird's-Eye-View (BEV) perception often struggles between adopting 3D-to-2D or 2D-to-3D view transformation (VT).
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Reading, C., Harakeh, A., Chae, J., Waslander, S.L.: Categorical depth distributionnetwork for monocular 3d object detection. In: CVPR (2021)
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Li, Y., Chen, Y., Qi, X., Li, Z., Sun, J., Jia, J.: Unifying voxel-based representation with transformer for 3d object detection. In: NeurIPS (2022)
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Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Qiao, Y., Dai, J.: Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In: ECCV (2022)
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Jinqing, Z., Yanan, Z., Qingjie, L., Yunhong, W.: Sa-bev: Generating semantic-aware bird’s-eye-view feature for multi-view 3d object detection. In: ICCV (2023)
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Li, Y., Bao, H., Ge, Z., Yang, J., Sun, J., Li, Z.: Bevstereo: Enhancing depth estimation in multi-view 3d object detection with dynamic temporal stereo. In: AAAI (2023)
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Li, Y., Ge, Z., Yu, G., Yang, J., Wang, Z., Shi, Y., Sun, J., Li, Z.: Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. In: AAAI (2023)
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Li, Z., Yu, Z., Wang, W., Anandkumar, A., Lu, T., Alvarez, J.M.: FB-BEV: BEV representation from forward-backward view transformations. In: ICCV (2023)
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2022
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Liu, Y., Wang, T., Zhang, X., Sun, J.: Petr: Position embedding transformation for multi-view 3d object detection. In: ECCV (2022)
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Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: CVPR (2022)
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Wang, Y., Guizilini, V.C., Zhang, T., Wang, Y., Zhao, H., Solomon, J.: Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In: CoRL (2022)
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Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G.: Vision transformer with deformable attention. In: CVPR (2022)
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Harley, A.W., Fang, Z., Li, J., Ambrus, R., Fragkiadaki, K.: Simple-BEV: What really matters for multi-sensor bev perception? In: ICRA (2023)
2023
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2023
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Liu, H., Teng, Y., Lu, T., Wang, H., Wang, L.: Sparsebev: High-performance sparse 3d object detection from multi-camera videos. In: ICCV (2023)
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Liu, Y., Yan, J., Jia, F., Li, S., Gao, Q., Wang, T., Zhang, X., Sun, J.: Petrv2: A unified framework for 3d perception from multi-camera images. In: ICCV (2023)
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Park, J., Xu, C., Yang, S., Keutzer, K., Kitani, K., Tomizuka, M., Zhan, W.: Time will tell: New outlooks and a baseline for temporal multi-view 3d object detection. In: ICLR (2023)
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Xiaowei, C., Jiaming, L., Lu, M., Zhang, R., Wang, Z., Guo, Y., Zhang, S.: Bev-san: Accurate bev 3d object detection via slice attention networks. In: CVPR (2023)
2023
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Xiong, K., Gong, S., Ye, X., Tan, X., Wan, J., Ding, E., Wang, J., Bai, X.: Cape: Camera view position embedding for multi-view 3d object detection. In: CVPR (2023)
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Yang, C., Chen, Y., Tian, H., Tao, C., Zhu, X., Zhang, Z., Huang, G., Li, H., Qiao, Y., Lu, L., Zhou, J., Dai, J.: Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision. In: CVPR (2023)
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Zhou, H., Ge, Z., Li, Z., Zhang, X.: Matrixvt: Efficient multi-camera to bev transformation for 3d perception. In: ICCV (2023)
2023
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