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The accurate reconstruction of dynamic scenes with neural radiance fields is significantly dependent on the estimation of camera poses.
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A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 318–10 327
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2021
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Q. Meng, A. Chen, H. Luo, M. Wu, H. Su, L. Xu, X. He, and J. Yu, “GNeRF: GAN-Based Neural Radiance Field Without Posed Camera,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , Oct. 2021, pp. 6351–6361
2021
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L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin, “iNeRF: Inverting Neural Radiance Fields for Pose Estimation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 1323–1330
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Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 786–12 796
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2023
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Y.-L. Liu, C. Gao, A. Meuleman, H.-Y. Tseng, A. Saraf, C. Kim, Y.-Y. Chuang, J. Kopf, and J.-B. Huang, “Robust dynamic radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13–23
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J. Wang, C. Rupprecht, and D. Novotny, “Posediffusion: Solving pose estimation via diffusion-aided bundle adjustment,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9773–9783
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Z. Zhu, S. Peng, V. Larsson, Z. Cui, M. R. Oswald, A. Geiger, and M. Pollefeys, “Nicer-slam: Neural implicit scene encoding for rgb slam,” in 2024 International Conference on 3D Vision (3DV) . IEEE, 2024, pp. 42–52
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H. Matsuki, R. Murai, P. H. Kelly, and A. J. Davison, “Gaussian splatting slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 18 039–18 048
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2024
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C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “Gs-slam: Dense visual slam with 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 595–19 604
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H. Schieber, F. Deuser, B. Egger, N. Oswald, and D. Roth, “Nerftrinsic four: An end-to-end trainable nerf jointly optimizing diverse intrinsic and extrinsic camera parameters,” Computer Vision and Image Understanding , vol. 249, p. 104206, 2024
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G. Wu, T. Yi, J. Fang, L. Xie, X. Zhang, W. Wei, W. Liu, Q. Tian, and X. Wang, “4d gaussian splatting for real-time dynamic scene rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 20 310–20 320
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X. Yu, R. Shen, K. Wu, and Z. Lin, “Robust visual slam in dynamic environment based on motion detection and segmentation,” Journal of Autonomous Vehicles and Systems , 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:270957414
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J. Li, X. Pan, G. Huang, Z. Zhang, N. Wang, H. Bao, and G. Zhang, “Rd-vio: Robust visual-inertial odometry for mobile augmented reality in dynamic environments,” IEEE Transactions on Visualization and Computer Graphics , 2024
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