[24] D. Rückert, L. Franke, and M. Stamminger. Adop: Approximate differentiable one-pixel point rendering. ACM Trans. Graph., 41(4):99:1-14, July 2022. ISSN 0730-0301.
[25] Sara Fridovich-Keil and Alex Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022.
[26] C. Sun, M. Sun, and H. Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In CVPR, 2022.
[27] M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar. Block-NeRF: Scalable large scene neural view synthesis. In CVPR, pages 8248–8258, June 2022.
[28] M. Tancik, E. Weber, E. Ng, R. Li, B. Yi, J. Kerr, T. Wang, A. Kristoffersen, J. Austin, K. Salahi, A. Ahuja, D. McAllister, and A. Kanazawa. Nerfstudio: A modular framework for neural radiance field development. In ACM SIGGRAPH 2023 Conference Proceedings, SIGGRAPH '23, 2023.
[29] V. Tschernezki, I. Laina, D. Larlus, and A. Vedaldi. Neural Feature Fusion Fields: 3D distillation of self-supervised 2D image representation. In International Conference on 3D Vision (3DV), 2022.
[30] H. Turki, D. Ramanan, and M. Satyanarayanan. Mega-NERF: Scalable construction of large-scale NeRFs for virtual fly-throughs. In CVPR, pages 12922-12931, June 2022.
[31] H. Turki, J. Y. Zhang, F. Ferroni, and D. Ramanan. SUDS: Scalable urban dynamic scenes. In CVPR, 2023.
[32] J. Wang, B. Amos, A. Das, P. Pillai, N. Sadeh, and M. Satyanarayanan. A scalable and privacy-aware IoT service for live video analytics. In Multimedia Systems Conference, pages 38-49, 2017.
[33] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004.
[34] L. Williams. Pyramidal parametrics. Computer Graphics, 17:1-11, July 1983.
[35] B. Wilson, W. Qi, T. Agarwal, J. Lambert, J. Singh, S. Khandelwal, B. Pan, R. Kumar, A. Hartnett, J. K. Pontes, D. Ramanan, P. Carr, and J. Hays. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In NeurIPS Datasets and Benchmarks, 2021.
[36] Y. Xiangli, L. Xu, X. Pan, N. Zhao, A. Rao, C. Theobalt, B. Dai, and D. Lin. BungeeNeRF: Progressive neural radiance field for extreme multi-scale scene rendering. In ECCV, 2022.
[37] H. Xin, Z. Qi, F. Ying, L. Xiaoyu, W. Xuan, and W. Qing. Local implicit ray function for generalizable radiance field representation. In CVPR, 2023.
[38] L. Xu, Y. Xiangli, S. Peng, X. Pan, N. Zhao, C. Theobalt, B. Dai, and D. Lin. Grid-guided neural radiance fields for large urban scenes. In CVPR, 2023.
[39] A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa. PlenOctrees for real-time rendering of neural radiance fields. In ICCV, 2021.
[40] K. Zhang, G. Riegler, N. Snavely, and V. Koltun. NeRF++: Analyzing and improving neural radiance fields. arXiv:2010.07492, 2020.
[41] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018.
[42] S. Zhi, T. Laidlow, S. Leutenegger, and A. Davison. In-place scene labelling and understanding with implicit scene representation. In ICCV, 2021.