Fetching the paper…
Reading the bibliography…
Recent advances in neural rendering have enabled highly photorealistic 3D scene reconstruction and novel view synthesis.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: ICCV. pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Ortiz-Cayon, R., Kalantari, N.K., Ramamoorthi, R., Ng, R., Kar, A.: Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM TOG (2019)
2019
Earlier work this paper cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché Buc, F., Fox, E., Garnett, R. (eds.) NeurIPS. pp. 8024–8035. Curran Associates, Inc. (2019)
2019
Earlier work this paper cites.
Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.: Video enhancement with task-oriented flow. IJCV 127
2019
Earlier work this paper cites.
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)
2020
Earlier work this paper cites.
Riba, E., Mishkin, D., Ponsa, D., Rublee, E., Bradski, G.: Kornia: an open source differentiable computer vision library for pytorch. In: Winter Conference on Applications of Computer Vision (2020)
2020
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: CVPR (2022)
2022
Cited alongside, same era.
Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: Tensorf: Tensorial radiance fields. In: ECCV (2022)
2022
Cited alongside, same era.
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: CVPR. pp. 17683–17693 (June 2022)
2022
Cited alongside, same era.
Jiang, Y., Hedman, P., Mildenhall, B., Xu, D., Barron, J.T., Wang, Z., Xue, T.: Alignerf: High-fidelity neural radiance fields via alignment-aware training. In: CVPR. pp. 46–55 (2023)
2023
Later among the works it cites.
Tancik, M., Weber, E., Ng, E., Li, R., Yi, B., Kerr, J., Wang, T., Kristoffersen, A., Austin, J., Salahi, K., Ahuja, A., McAllister, D., Kanazawa, A.: Nerfstudio: A modular framework for neural radiance field development. In: ACM SIGGRAPH 2023 Conference Proceedings. SIGGRAPH ’23 (2023)
2023
Later among the works it cites.
Zhou, K., Li, W., Jiang, N., Han, X., Lu, J.: From nerflix to nerflix++: A general nerf-agnostic restorer paradigm. IEEE TPAMI pp. 1–17 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…