[23] B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ramamoorthi, R. Ng, and A. Kar. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. TOG, 38(4):1-14, 2019.
[24] 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 Eur. Conf. Comput. Vis., 2020.
[25] T. Müller, A. Evans, C. Schied, and A. Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., pages 1-15, 2022.
[26] N. M. Nasrabadi and R. A. King. Image coding using vector quantization: A review. IEEE Transactions on communications, 36(8):957-971, 1988.
[27] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon. KinectFusion: Real-time dense surface mapping and tracking. In ISMAR, pages 127-136, 2011.
[28] 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 Int. Conf. Comput. Vis., pages 5865-5874, 2021.
[29] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. In Adv. Neural Inform. Process. Syst., pages 8026-8037, 2019.
[30] S. Peng, Y. Yan, Q. Shuai, H. Bao, and X. Zhou. Representing volumetric videos as dynamic mlp maps. In IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[31] A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10318-10327, 2021.
[32] C. Reiser, S. Peng, Y. Liao, and A. Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlp's. In Int. Conf. Comput. Vis., pages 14335-14345, 2021.
[33] C. Reiser, R. Szeliski, D. Verbin, P. P. Srinivasan, B. Mildenhall, A. Geiger, J. T. Barron, and P. Hedman. Merf: Memory-efficient radiance fields for real-time view synthesis in unbounded scenes. arXiv preprint arXiv:2302.12249, 2023.
[34] D. Rho, B. Lee, S. Nam, J. C. Lee, J. H. Ko, and E. Park. Masked wavelet representation for compact neural radiance fields. In IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[35] Sara Fridovich-Keil and Giacomo Meanti, F. R. Warburg, B. Recht, and A. Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[36] J. L. Schonberger and J.-M. Frahm. Structure-from-motion revisited. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4104-4113, 2016.
[37] J. L. Schonberger, E. Zheng, J.-M. Frahm, and M. Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In Eur. Conf. Comput. Vis., pages 501-518, 2016.
[38] R. Shao, Z. Zheng, H. Tu, B. Liu, H. Zhang, and Y. Liu. Tensor4d: Efficient neural 4d decomposition for high-fidelity dynamic reconstruction and rendering. In IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[39] T. Sikora. Thempeg-4 video standard verification model. IEEE Transactions on circuits and systems for video technology, 7(1):19-31, 1997.
[40] L. Song, A. Chen, Z. Li, Z. Chen, L. Chen, J. Yuan, Y. Xu, and A. Geiger. Nerfplayer: A streamable dynamic scene representation with decomposed neural radiance fields. ACM Trans. Graph., pages 2732-2742, 2023.
[41] C. Sun, M. Sun, and H.-T. Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5459-5469, 2022.
[42] T. Takikawa, A. Evans, J. Tremblay, T. Müller, M. McGuire, A. Jacobson, and S. Fidler. Variable bitrate neural fields. In SIGGRAPH, pages 1-9, 2022.
[43] J. Tang, X. Chen, J. Wang, and G. Zeng. Compressible-composable nef via rank-residual decomposition. arXiv preprint arXiv:2205.14870, 2022.
[44] A. Van Den Oord, O. Vinyals, et al. Neural discrete representation learning. In Adv. Neural Inform. Process. Syst., 2017.