SDF-Net: Enhanced Novel View Synthesis from Ultra-sparse Viewpoints via Multi-level Feature Fusion
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5588-2_34
Reference20 articles.
1. Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: RegNeRF: regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5480–5490 (2022)
2. 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)
3. Kulh ́anek, J., Derner, E., Sattler, T., Babuˇska, R.: Viewformer: NeRF-free neural rendering from few images using transformers. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, vol. 13675, pp. 198–216. Springer (2022). https://doi.org/10.1007/978-3-031-19784-0_12
4. 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)
5. Chen, A., et al.: 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)
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