[87] Q. Wang, Z. Wang, K. Genova, P. Srinivasan, H. Zhou, J. T. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser. Ibrnet: Learning multi-view image-based rendering. arXiv preprint arXiv:2102.13090, 2021.
[88] T. Wang, B. Zhang, T. Zhang, S. Gu, J. Bao, T. Baltrusaitis, J. Shen, D. Chen, F. Wen, Q. Chen, et al. Rodin: A generative model for sculpting 3d digital avatars using diffusion. arXiv preprint arXiv:2212.06135, 2022.
[89] Z. Wang, S. Wu, W. Xie, M. Chen, and V. A. Prisacariu. NeRF--: Neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064, 2021.
[90] Z. Wang, L. Li, Z. Shen, L. Shen, and L. Bo. 4k-nerf: High fidelity neural radiance fields at ultra high resolutions. arXiv preprint arXiv:2212.04701, 2022.
[91] W. Xian, J.-B. Huang, J. Kopf, and C. Kim. Space-time neural irradiance fields for free-viewpoint video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9421-9431, 2021.
[92] D. Xu, Y. Jiang, P. Wang, Z. Fan, H. Shi, and Z. Wang. Sinnerf: Training neural radiance fields on complex scenes from a single image. 2022.
[93] L. Yariv, P. Hedman, C. Reiser, D. Verbin, P. P. Srinivasan, R. Szeliski, J. T. Barron, and B. Mildenhall. Bakedsdf: Meshing neural sdfs for real-time view synthesis. arXiv preprint arXiv:2302.14859, 2023.
[94] L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin. iNeRF: Inverting neural radiance fields for pose estimation. https://arxiv.org/abs/2012.05877, 2020.
[95] Z.-X. Yin, J. Qiu, M.-M. Cheng, and B. Ren. Multi-space neural radiance fields. 2023.
[96] A. Yu, S. Fridovich-Keil, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa. Plenoxels: Radiance fields without neural networks. arXiv preprint arXiv:2112.05131, 2021.
[97] A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa. Plenoctrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752-5761, 2021.
[98] A. Yu, V. Ye, M. Tancik, and A. Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In CVPR, 2021.
[99] W. Yuan, Z. Lv, T. Schmidt, and S. Lovegrove. Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13144–13152, 2021.
[100] K. Zhang, G. Riegler, N. Snavely, and V. Koltun. Nerf++: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492, 2020.
[101] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018.
[102] F. Zhao, W. Yang, J. Zhang, P. Lin, Y. Zhang, J. Yu, and L. Xu. Humannerf: Efficiently generated human radiance field from sparse inputs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7743-7753, June 2022.
[103] Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys. Nice-slam: Neural implicit scalable encoding for slam, 2021.
[104] C. L. Zitnick, S. B. Kang, M. Uytendaele, S. Winder, and R. Szeliski. High-quality video view interpolation using a layered representation. ACM transactions on graphics (TOG), 23(3):600-608, 2004.