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Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities.
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J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang, and D. Cai, “Mononerd: Nerf-like representations for monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6814–6824
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Z. Xie, Z. Pang, and Y.-X. Wang, “Mv-map: Offboard hd-map generation with multi-view consistency,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8658–8668
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H. Turki, J. Y. Zhang, F. Ferroni, and D. Ramanan, “Suds: Scalable urban dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 12 375–12 385
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J. Deng, Q. Wu, X. Chen, S. Xia, Z. Sun, G. Liu, W. Yu, and L. Pei, “Nerf-loam: Neural implicit representation for large-scale incremental lidar odometry and mapping,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8218–8227
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X. Liu, Y. Li, Y. Teng, H. Bao, G. Zhang, Y. Zhang, and Z. Cui, “Multi-modal neural radiance field for monocular dense slam with a light-weight tof sensor,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 1–11
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E. Sandström, Y. Li, L. Van Gool, and M. R. Oswald, “Point-slam: Dense neural point cloud-based slam,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 18 433–18 444
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Z. Yang, Y. Chen, J. Wang, S. Manivasagam, W.-C. Ma, A. J. Yang, and R. Urtasun, “Unisim: A neural closed-loop sensor simulator,” in CVPR , 2023
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