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NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints.
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Chen, D., Liu, Y., Huang, L., Wang, B., Pan, P.: GeoAug: Data Augmentation for Few-Shot NeRF with Geometry Constraints. In: ECCV (2022)
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Chen, T., Wang, P., Fan, Z., Wang, Z.: Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations. In: CVPR (2022)
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Ichnowski, J., Avigal, Y., Kerr, J., Goldberg, K.: Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects. In: CRL (2022)
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Cited alongside, same era.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In: ECCV (2020)
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Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields. In: ICCV (2021)
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Hedman, P., Srinivasan, P.P., Mildenhall, B., Barron, J.T., Debevec, P.: Baking Neural Radiance Fields for Real-Time View Synthesis. In: ICCV (2021)
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Jain, A., Tancik, M., Abbeel, P.: Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis. In: ICCV (2021)
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Müller, T., Rousselle, F., Novák, J., Keller, A.: Real-time neural radiance caching for path tracing. ACM Transactions on Graphics
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Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S.M., Geiger, A., Radwan, N.: RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs. In: CVPR (2022)
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Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., Yu, P.S.: Generalizing to Unseen Domains: A Survey on Domain Generalization. IEEE Transactions on Knowledge & Data Engineering
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