[1] E. Adelson, C. Anderson, J. Bergen, P. Burt, and J. Ogden. Pyramid methods in image processing. RCA Eng., 29, 11 1983.
[2] J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan. Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, 2021.
[3] J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, 2022.
[4] J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman. Zip-NeRF: Anti-aliased grid-based neural radiance fields. In ICCV, 2023.
[5] P. Burt and E. Adelson. The laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4):532-540, 1983. doi: 10.1109/TCOM.1983.1095851.
[6] A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su. TensoRF: Tensorial radiance fields. In ECCV, 2022.
[7] B. Cheng, M. D. Collins, Y. Zhu, T. Liu, T. S. Huang, H. Adam, and L.-C. Chen. Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. In CVPR, 2020.
[8] C. Crassin, F. Neyret, M. Sainz, S. Green, and E. Eisemann. Interactive indirect illumination using voxel cone tracing: A preview. In Symposium on Interactive 3D Graphics and Games, 2011.
[9] S. Fridovich-Keil, G. Meanti, F. R. Warburg, B. Recht, and A. Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In CVPR, 2023.
[10] S. J. Garbin, M. Kowalski, M. Johnson, J. Shotton, and J. Valentin. FastNeRF: High-fidelity neural rendering at 200fps. In ICCV, 2021.
[11] P. Hedman, P. P. Srinivasan, B. Mildenhall, J. T. Barron, and P. Debevec. Baking neural radiance fields for real-time view synthesis. In ICCV, 2021.
[12] D. Hu, Z. Zhang, T. Hou, T. Liu, H. Fu, and M. Gong. Multiscale representation for real-time anti-aliasing neural rendering. arxiv:2304.10075, 2023.
[13] W. Hu, Y. Wang, L. Ma, B. Yang, L. Gao, X. Liu, and Y. Ma. Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In ICCV, 2023.
[14] B. K. S. Isaac-Medina, C. G. Willcocks, and T. P. Breckon. Exact-NeRF: An exploration of a precise volumetric parameterization for neural radiance fields. CVPR, 2023.
[15] J. Kerr, C. M. Kim, K. Goldberg, A. Kanazawa, and M. Tancik. LERF: Language embedded radiance fields. In ICCV, 2023.
[16] S. Kobayashi, E. Matsumoto, and V. Sitzmann. Decomposing nef for editing via feature field distillation. In Advances in Neural Information Processing Systems, volume 35, 2022.
[17] L. Liu, J. Gu, K. Z. Lin, T.-S. Chua, and C. Theobalt. Neural sparse voxel fields. NIPS, 2020.
[18] 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.
[19] 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. In ECCV, 2020.
[20] T. Müller, A. Evans, C. Schied, and A. Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-15, July 2022. doi: 10.1145/3528223.3530127.
[21] J. Philip and V. Deschaintre. Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training. In T. Ritschel and A. Weidlich, editors, Eurographics Symposium on Rendering. The Eurographics Association, 2023. ISBN 978-3-03868-229-5. doi: 10.2312/sr.20231122.
[22] D. Rebain, W. Jiang, S. Yazdani, K. Li, K. Yi, and A. Tagliasacchi. DeRF: Decomposed radiance fields. In CVPR, 2021.
[23] C. Reiser, S. Peng, Y. Liao, and A. Geiger. KiloNeRF: Speeding up neural radiance fields with thousands of tiny MLPs. In ICCV, 2021.