[43] C.-H. Lin, W.-C. Ma, A. Torralba, and S. Lucey. Barf: Bundle-adjusting neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), 2021.
[44] D. B. Lindell, J. N. Martel, and G. Wetzstein. Autoint: Automatic integration for fast neural volume rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14556-14565, 2021.
[45] H.-K. Liu, I.-C. Shen, and B.-Y. Chen. Nerf-in: Free-form nerf inpainting with rgb-d priors. arxiv preprint arXiv:2206.04901, 2022.
[46] J.-W. Liu, Y.-P. Cao, W. Mao, W. Zhang, D. J. Zhang, J. Keppo, Y. Shan, X. Qie, and M. Z. Shou. Devrf: Fast deformable voxel radiance fields for dynamic scenes. arXiv preprint arXiv:2205.15723, 2022.
[47] L. Liu, J. Gu, K. Zaw Lin, T.-S. Chua, and C. Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020.
[48] S. Liu, X. Zhang, Z. Zhang, R. Zhang, J.-Y. Zhu, and B. Russell. Editing conditional radiance fields, 2021.
[49] Y.-L. Liu, C. Gao, A. Meuleman, H.-Y. Tseng, A. Saraf, C. Kim, Y.-Y. Chuang, J. Kopf, and J.-B. Huang. Robust dynamic radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[50] S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh. Neural volumes: Learning dynamic renderable volumes from images. arXiv preprint arXiv:1906.07751, 2019.
[51] S. Lombardi, T. Simon, G. Schwartz, M. Zollhoefer, Y. Sheikh, and J. Saragih. Mixture of volumetric primitives for efficient neural rendering, 2021.
[52] L. Ma, X. Li, J. Liao, Q. Zhang, X. Wang, J. Wang, and P. V. Sander. Deblur-nerf: Neural radiance fields from blurry images. arXiv preprint arXiv:2111.14292, 2021.
[53] R. Martin-Brualla, N. Radwan, M. S. Sajjadi, J. T. Barron, A. Dosovitskiy, and D. Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210–7219, 2021.
[54] R. Martin-Brualla, N. Radwan, M. S. M. Sajjadi, J. T. Barron, A. Dosovitskiy, and D. Duckworth. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. In CVPR, 2021.
[55] Q. Meng, A. Chen, H. Luo, M. Wu, H. Su, L. Xu, X. He, and J. Yu. Gnerf: Gan-based neural radiance field without posed camera. arXiv preprint arXiv:2103.15606, 2021.
[56] B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron. NeRF in the dark: High dynamic range view synthesis from noisy raw images. arXiv, 2021.
[57] B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.
[58] T. Müller, A. Evans, C. Schied, and A. Keller. Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989, 2022.
[59] M. Niemeyer and A. Geiger. Campari: Camera-aware decomposed generative neural radiance fields, 2021.
[60] M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. M. Sajjadi, A. Geiger, and N. Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022.
[61] M. Nießner, M. Zollhöfer, S. Izadi, and M. Stamminger. Real-time 3d reconstruction at scale using voxel hashing. ACM Trans. Graph., 32(6):169:1-169:11, 2013. doi: 10.1145/2508363.2508374. URL https://doi.org/10.1145/2508363.2508374.
[62] X. Pan, X. Xu, C. C. Loy, C. Theobalt, and B. Dai. A shading-guided generative implicit model for shape-accurate 3d-aware image synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[63] K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R. Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021.
[64] K. Park, U. Sinha, P. Hedman, J. T. Barron, S. Bouaziz, D. B. Goldman, R. Martin-Brualla, and S. M. Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021.
[65] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Z. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. B. Fox, and