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Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy.
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Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelnerf: Neural radiance fields from one or few images. In: CVPR. pp. 4578–4587 (2021)
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Chng, S.F., Ramasinghe, S., Sherrah, J., Lucey, S.: Gaussian activated neural radiance fields for high fidelity reconstruction and pose estimation. In: European Conference on Computer Vision. pp. 264–280. Springer (2022)
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Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised NeRF: Fewer views and faster training for free. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2022)
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Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance fields without neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5501–5510 (June 2022)
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Hamdi, A., Ghanem, B., Nießner, M.: Sparf: Large-scale learning of 3d sparse radiance fields from few input images. arxiv (2022)
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Kim, M., Seo, S., Han, B.: Infonerf: Ray entropy minimization for few-shot neural volume rendering. In: CVPR. pp. 12912–12921 (2022)
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Lin, C.H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fidler, S., Liu, M.Y., Lin, T.Y.: Magic3d: High-resolution text-to-3d content creation. In: CVPR (2023)
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Philip, J., Deschaintre, V.: Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training. In: Ritschel, T., Weidlich, A. (eds.) Eurographics Symposium on Rendering. The Eurographics Association (2023). https://doi.org/10.2312/sr.20231122
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Rosinol, A., Leonard, J.J., Carlone, L.: Nerf-slam: Real-time dense monocular slam with neural radiance fields. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 3437–3444. IEEE (2023)
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