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Mildenhall, P.P. Srinivasan, M. Tancik, J.T. Barron, R. Ramamoorthi, and R. Ng, \u201cNeRF: Representing scenes as neural radiance fields for view synthesis,\u201d Proc. European Conference on Computer Vision, pp.405-421, 2020. 10.1007\/978-3-030-58452-8_24","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] L. Wu, J.Y. Lee, A. Bhattad, Y.X. Wang, and D. Forsyth, \u201cDIVeR: Real-time and accurate neural radiance fields with deterministic integration for volume rendering,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.16200-16209, 2022. 10.1109\/cvpr52688.2022.01572","DOI":"10.1109\/CVPR52688.2022.01572"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] B. Mildenhall, P. Hedman, R. Martin-Brualla, P.P. Srinivasan, and J.T. Barron, \u201cNeRF in the dark: High dynamic range view synthesis from noisy raw images,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.16190-16199, 2022. 10.1109\/cvpr52688.2022.01571","DOI":"10.1109\/CVPR52688.2022.01571"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, \u201cD-NeRF: Neural radiance fields for dynamic scenes,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.10318-10327, 2021. 10.1109\/cvpr46437.2021.01018","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. Era, R. Togo, K. Maeda, T. Ogawa, and M. Haseyama, \u201cContent-based image retrieval using effective synthesized images from different camera views via pixelnerf,\u201d IEEE 11th Global Conference on Consumer Electronics (GCCE), pp.404-405, IEEE, 2022. 10.1109\/gcce56475.2022.10014304","DOI":"10.1109\/GCCE56475.2022.10014304"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] T. Hu, S. Liu, Y. Chen, T. Shen, and J. Jia, \u201cEfficientNeRF efficient neural radiance fields,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.12902-12911, 2022. 10.1109\/cvpr52688.2022.01256","DOI":"10.1109\/CVPR52688.2022.01256"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, \u201cImage quality assessment: From error visibility to structural similarity,\u201d IEEE Trans. Image Process., vol.13, no.4, pp.600-612, 2004. 10.1109\/tip.2003.819861","DOI":"10.1109\/TIP.2003.819861"},{"key":"8","unstructured":"[8] Z. Wang, E.P. Simoncelli, and A.C. Bovik, \u201cMultiscale structural similarity for image quality assessment,\u201d The Thrity-Seventh Asilomar Conference on Signals, Systems &amp; Computers, 2003, pp.1398-1402, 2003. 10.1109\/acssc.2003.1292216"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] R. Zhang, P. Isola, A.A. Efros, E. Shechtman, and O. Wang, \u201cThe unreasonable effectiveness of deep features as a perceptual metric,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.586-595, 2018. 10.1109\/cvpr.2018.00068","DOI":"10.1109\/CVPR.2018.00068"},{"key":"10","unstructured":"[10] L. Wang, \u201cA survey on IQA,\u201d arXiv preprint, arXiv:2109.00347, 2021. 10.48550\/arXiv.2109.00347"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] T. Huang, J. Burnett, and A. Deczky, \u201cThe importance of phase in image processing filters,\u201d IEEE Trans. Acoust., Speech, Signal Process., vol.23, no.6, pp.529-542, 1975. 10.1109\/tassp.1975.1162738","DOI":"10.1109\/TASSP.1975.1162738"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] A.V. Oppenheim and J.S. Lim, \u201cThe importance of phase in signals,\u201d Proc. IEEE, vol.69, no.5, pp.529-541, 1981. 10.1109\/proc.1981.12022","DOI":"10.1109\/PROC.1981.12022"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] T. Hu, S. Liu, Y. Chen, T. Shen, and J. Jia, \u201cEfficientNeRF efficient neural radiance fields,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.12902-12911, 2022. 10.1109\/cvpr52688.2022.01256","DOI":"10.1109\/CVPR52688.2022.01256"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] L. Wang, J. Zhang, X. Liu, F. Zhao, Y. Zhang, Y. Zhang, M. Wu, J. Yu, and L. Xu, \u201cFourier plenoctrees for dynamic radiance field rendering in real-time,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.13524-13534, 2022. 10.1109\/cvpr52688.2022.01316","DOI":"10.1109\/CVPR52688.2022.01316"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C. Wang, M. Chai, M. He, D. Chen, and J. Liao, \u201cClip-NeRF: Text-and-image driven manipulation of neural radiance fields,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.3835-3844, 2022. 10.1109\/cvpr52688.2022.00381","DOI":"10.1109\/CVPR52688.2022.00381"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. Kania, K.M. Yi, M. Kowalski, T. Trzci\u0144ski, and A. Tagliasacchi, \u201cCoNeRF: Controllable neural radiance fields,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.18623-18632, 2022. 10.1109\/cvpr52688.2022.01807","DOI":"10.1109\/CVPR52688.2022.01807"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Y.J. Yuan, Y.T. Sun, Y.K. Lai, Y. Ma, R. Jia, and L. Gao, \u201cNeRF-editing: Geometry editing of neural radiance fields,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.18353-18364, 2022. 10.1109\/cvpr52688.2022.01781","DOI":"10.1109\/CVPR52688.2022.01781"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] C. Gao, A. Saraf, J. Kopf, and J.B. Huang, \u201cDynamic view synthesis from dynamic monocular video,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.5712-5721, 2021. 10.1109\/iccv48922.2021.00566","DOI":"10.1109\/ICCV48922.2021.00566"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] K. Park, U. Sinha, J.T. Barron, S. Bouaziz, D.B. Goldman, S.M. Seitz, and R. Martin-Brualla, \u201cNerfies: Deformable neural radiance fields,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.5865-5874, 2021. 10.1109\/iccv48922.2021.00581","DOI":"10.1109\/ICCV48922.2021.00581"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] K. Park, U. Sinha, P. Hedman, J.T. Barron, S. Bouaziz, D.B. Goldman, R. Martin-Brualla, and S.M. Seitz, \u201cHyperNeRF: A higher-dimensional representation for topologically varying neural radiance fields,\u201d arXiv preprint arXiv:2106.13228, 2021. 10.48550\/arXiv.2106.13228","DOI":"10.1145\/3478513.3480487"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M. Kawai, R. Yanagi, R. Togo, T. Ogawa, and M. Haseyama, \u201cFree-viewpoint sports video generation based on dynamic NeRF considering time series,\u201d Proc. 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE), pp.408-409, 2022. 10.1109\/gcce56475.2022.10014370","DOI":"10.1109\/GCCE56475.2022.10014370"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] A.M. Demirtas, A.R. Reibman, and H. Jafarkhani, \u201cFull-reference quality estimation for images with different spatial resolutions,\u201d IEEE Trans. Image Process., vol.23, no.5, pp.2069-2080, 2014. 10.1109\/tip.2014.2310991","DOI":"10.1109\/TIP.2014.2310991"},{"key":"23","unstructured":"[23] Z. Wang, A.C. Bovik, and B.L. Evan, \u201cBlind measurement of blocking artifacts in images,\u201d Proc. International Conference on Image Processing, pp.981-984, 2000. 10.1109\/icip.2000.899622"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] C. Liu, W.T. Freeman, R. Szeliski, and S.B. Kang, \u201cNoise estimation from a single image,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.901-908, 2006. 10.1109\/cvpr.2006.207","DOI":"10.1109\/CVPR.2006.207"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] A. Mittal, A.K. Moorthy, and A.C. Bovik, \u201cNo-reference image quality assessment in the spatial domain,\u201d IEEE Trans. Image Process., vol.21, no.12, pp.4695-4708, 2012. 10.1109\/tip.2012.2214050","DOI":"10.1109\/TIP.2012.2214050"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] J.A. Redi, P. Gastaldo, I. Heynderickx, and R. Zunino, \u201cColor distribution information for the reduced-reference assessment of perceived image quality,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.20, no.12, pp.1757-1769, 2010. 10.1109\/tcsvt.2010.2087456","DOI":"10.1109\/TCSVT.2010.2087456"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] X. Gao, W. Lu, X. Li, and D. Tao, \u201cWavelet-based contourlet in quality evaluation of digital images,\u201d Neurocomputing, vol.72, no.1-3, pp.378-385, 2008. 10.1016\/j.neucom.2007.12.031","DOI":"10.1016\/j.neucom.2007.12.031"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] L. Ma, S. Li, F. Zhang, and K.N. Ngan, \u201cReduced-reference image quality assessment using reorganized dct-based image representation,\u201d IEEE Trans. Multimedia, vol.13, no.4, pp.824-829, 2011. 10.1109\/tmm.2011.2109701","DOI":"10.1109\/TMM.2011.2109701"},{"key":"29","unstructured":"[29] J.S. Yoon, K. Kim, O. Gallo, H.S. Park, and J. Kautz, \u201cNovel view synthesis of dynamic scenes with globally coherent depths from a monocular camera,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.5336-5345, 2020. 10.1109\/cvpr42600.2020.00538"},{"key":"30","unstructured":"[30] J.S. Yoon, K. Kim, O. Gallo, H.S. Park, and J. Kautz, \u201cNovel view synthesis of dynamic scenes with globally coherent depths from a monocular camera,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.5336-5345, 2020. 10.1109\/cvpr42600.2020.00538"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] Z. Li, S. Niklaus, N. Snavely, and O. Wang, \u201cNeural scene flow fields for space-time view synthesis of dynamic scenes,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.6498-6508, 2021. 10.1109\/cvpr46437.2021.00643","DOI":"10.1109\/CVPR46437.2021.00643"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] E. Tretschk, A. Tewari, V. Golyanik, M. Zollh\u00f6fer, C. Lassner, and C. Theobalt, \u201cNon-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.12959-12970, 2021. 10.1109\/iccv48922.2021.01272","DOI":"10.1109\/ICCV48922.2021.01272"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] A. Jain, M. Tancik, and P. Abbeel, \u201cPutting nerf on a diet: Semantically consistent few-shot view synthesis,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.5885-5894, 2021. 10.1109\/iccv48922.2021.00583","DOI":"10.1109\/ICCV48922.2021.00583"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] Y. Uchida, R. Togo, K. Maeda, T. Ogawa, and M. 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