Fetching the paper…
Reading the bibliography…
3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene.
Avidan, S., Shashua, A.: Novel view synthesis in tensor space. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1034–1040. IEEE (1997)
1997
Earlier work this paper cites.
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory. pp. 92–100 (1998)
1998
Earlier work this paper cites.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
Earlier work this paper cites.
Sindhwani, V., Niyogi, P., Belkin, M.: A co-regularization approach to semi-supervised learning with multiple views. In: Proceedings of ICML workshop on learning with multiple views. vol. 2005, pp. 74–79. Citeseer (2005)
2005
Earlier work this paper cites.
Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aanæs, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 406–413 (2014)
2014
Earlier work this paper cites.
Schönberger, J.L., Frahm, J.M.: Structure-from-Motion Revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: Pixelwise View Selection for Unstructured Multi-View Stereo. In: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
Zhou, T., Tulsiani, S., Sun, W., Malik, J., Efros, A.A.: View synthesis by appearance flow. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. pp. 286–301. Springer (2016)
2016
Earlier work this paper cites.
Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity invariant cnns. In: International Conference on 3D Vision (3DV) (2017)
2017
Earlier work this paper cites.
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., Sugiyama, M.: Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31
2018
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
Earlier work this paper cites.
2018
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 Transactions on Graphics (TOG) 38
2019
Earlier work this paper cites.
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., Sugiyama, M.: How does disagreement help generalization against label corruption? In: International Conference on Machine Learning. pp. 7164–7173. PMLR (2019)
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Liu, L., Gu, J., Zaw Lin, K., Chua, T.S., Theobalt, C.: Neural sparse voxel fields. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Wei, H., Feng, L., Chen, X., An, B.: Combating noisy labels by agreement: A joint training method with co-regularization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13726–13735 (2020)
2020
Earlier work this paper cites.
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: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5855–5864 (2021)
2021
Cited alongside, same era.
Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., Su, H.: Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14124–14133 (2021)
2021
Cited alongside, same era.
Chibane, J., Bansal, A., Lazova, V., Pons-Moll, G.: Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7911–7920 (2021)
2021
Cited alongside, same era.
Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: Semantically consistent few-shot view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5885–5894 (2021)
Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., Neumann, U.: Point-nerf: Point-based neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5438–5448 (2022)
2022
Later among the works it cites.
Chen, Z., Li, Z., Song, L., Chen, L., Yu, J., Yuan, J., Xu, Y.: Neurbf: A neural fields representation with adaptive radial basis functions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4182–4194 (2023)
2023
Later among the works it cites.
Cong, W., Liang, H., Wang, P., Fan, Z., Chen, T., Varma, M., Wang, Y., Wang, Z.: Enhancing nerf akin to enhancing llms: Generalizable nerf transformer with mixture-of-view-experts. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3193–3204 (2023)
2023
Later among the works it cites.
Hu, W., Wang, Y., Ma, L., Yang, B., Gao, L., Liu, X., Ma, Y.: Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19774–19783 (2023)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
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. Communications of the ACM 65
2021
Cited alongside, same era.
Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5752–5761 (2021)
2021
Cited alongside, same era.
Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelnerf: Neural radiance fields from one or few images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4578–4587 (2021)
2021
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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5470–5479 (2022)
2022
Cited alongside, same era.
Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: Tensorf: Tensorial radiance fields. In: Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII. pp. 333–350. Springer (2022)
2022
Cited alongside, same era.
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. pp. 12882–12891 (2022)
2022
Cited alongside, same era.
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. pp. 5501–5510 (2022)
2022
Cited alongside, same era.
Kim, M., Seo, S., Han, B.: Infonerf: Ray entropy minimization for few-shot neural volume rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12912–12921 (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (ToG) 42
2023
Later among the works it cites.
2023
Later among the works it cites.
Song, J., Park, S., An, H., Cho, S., Kwak, M.S., Cho, S., Kim, S.: Därf: Boosting radiance fields from sparse inputs with monocular depth adaptation (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, G., Chen, Z., Loy, C.C., Liu, Z.: Sparsenerf: Distilling depth ranking for few-shot novel view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 9065–9076 (October 2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wynn, J., Turmukhambetov, D.: Diffusionerf: Regularizing neural radiance fields with denoising diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4180–4189 (2023)
2023
Later among the works it cites.
Xiong, H., Muttukuru, S.: Sparsegs: Real-time 360° sparse view synthesis using gaussian splatting (10 2023)
2023
Later among the works it cites.
Yang, J., Pavone, M., Wang, Y.: Freenerf: Improving few-shot neural rendering with free frequency regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8254–8263 (2023)
2023
Later among the works it cites.
Zhou, Z., Tulsiani, S.: Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction. In: CVPR (2023)
2023
Later among the works it cites.
Zhu, Z., Fan, Z., Jiang, Y., Wang, Z.: Fsgs: Real-time few-shot view synthesis using gaussian splatting (2023)
2023
Later among the works it cites.
Li, J., Zhang, J., Bai, X., Zheng, J., Ning, X., Zhou, J., Gu, L.: Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20775–20785 (2024)
2024
Closest in time.
Zhang, J., Li, J., Huang, L., Yu, X., Gu, L., Zheng, J., Bai, X.: Robust synthetic-to-real transfer for stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20247–20257 (2024)
2024
Closest in time.