{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:41:54Z","timestamp":1742956914396,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031820205"},{"type":"electronic","value":"9783031820212"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-82021-2_10","type":"book-chapter","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T09:45:31Z","timestamp":1740822331000},"page":"146-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UrgRF:Radiance Field Reconstruction Guided by Low-Resolution Grids"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3386-625X","authenticated-orcid":false,"given":"Dezhi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weibing","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuyuan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,1]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5855\u20135864 (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Cao, A., Johnson, J.: HexPlane: a fast representation for dynamic scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 130\u2013141 (2023)","DOI":"10.1109\/CVPR52729.2023.00021"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chan, E.R., et\u00a0al.: Efficient geometry-aware 3D generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16123\u201316133 (2022)","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: TensoRF: tensorial radiance fields. In: Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XXXII, pp. 333\u2013350. Springer (2022)","DOI":"10.1007\/978-3-031-19824-3_20"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Dadon, D., Fried, O., Hel-Or, Y.: DDNeRF: depth distribution neural radiance fields. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 755\u2013763 (2023)","DOI":"10.1109\/WACV56688.2023.00082"},{"issue":"4","key":"10_CR6","doi-asserted-by":"publisher","first-page":"2482","DOI":"10.1109\/TCSVT.2021.3081591","volume":"32","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Wen, C., Wu, H., Guo, Y., Chen, L., Wang, C.: Indoor 3D human trajectory reconstruction using surveillance camera videos and point clouds. IEEE Trans. Circ. Syst. Video Technol. 32(4), 2482\u20132495 (2021)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Xiang, J., Tong, X.: GRAM: generative radiance manifolds for 3D-aware image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10673\u201310683 (2022)","DOI":"10.1109\/CVPR52688.2022.01041"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.patrec.2019.05.007","volume":"125","author":"H Dhamo","year":"2019","unstructured":"Dhamo, H., Tateno, K., Laina, I., Navab, N., Tombari, F.: Peeking behind objects: layered depth prediction from a single image. Pattern Recogn. Lett. 125, 333\u2013340 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"10_CR9","unstructured":"Fang, J., Xie, L., Wang, X., Zhang, X., Liu, W., Tian, Q.: NeuSample: neural sample field for efficient view synthesis. arXiv preprint arXiv:2111.15552 (2021)"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil, S., Meanti, G., Warburg, F., Recht, B., Kanazawa, A.: K-planes: explicit radiance fields in space, time, and appearance. arXiv preprint arXiv:2301.10241 (2023)","DOI":"10.1109\/CVPR52729.2023.01201"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Jain, A., Mildenhall, B., Barron, J.T., Abbeel, P., Poole, B.: Zero-shot text-guided object generation with dream fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 867\u2013876 (2022)","DOI":"10.1109\/CVPR52688.2022.00094"},{"issue":"3","key":"10_CR12","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1145\/964965.808594","volume":"18","author":"JT Kajiya","year":"1984","unstructured":"Kajiya, J.T., Von Herzen, B.P.: Ray tracing volume densities. ACM SIGGRAPH Comput. Graph. 18(3), 165\u2013174 (1984)","journal-title":"ACM SIGGRAPH Comput. Graph."},{"key":"10_CR13","first-page":"23311","volume":"35","author":"S Kobayashi","year":"2022","unstructured":"Kobayashi, S., Matsumoto, E., Sitzmann, V.: Decomposing NeRF for editing via feature field distillation. Adv. Neural. Inf. Process. Syst. 35, 23311\u201323330 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Kurz, A., Neff, T., Lv, Z., Zollh\u00f6fer, M., Steinberger, M.: AdaNeRF: adaptive sampling for real-time rendering of neural radiance fields. In: European Conference on Computer Vision, pp. 254\u2013270. Springer (2022)","DOI":"10.1007\/978-3-031-19790-1_16"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Levin, A., Durand, F.: Linear view synthesis using a dimensionality gap light field prior. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1831\u20131838. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539854"},{"issue":"3","key":"10_CR16","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1145\/78964.78965","volume":"9","author":"M Levoy","year":"1990","unstructured":"Levoy, M.: Efficient ray tracing of volume data. ACM Trans. Graph. (TOG) 9(3), 245\u2013261 (1990)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Li, T., et\u00a0al.: Neural 3D video synthesis from multi-view video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5521\u20135531 (2022)","DOI":"10.1109\/CVPR52688.2022.00544"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: BARF: bundle-adjusting neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5741\u20135751 (2021)","DOI":"10.1109\/ICCV48922.2021.00569"},{"issue":"6","key":"10_CR19","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/TCSVT.2014.2302379","volume":"24","author":"C Lipski","year":"2014","unstructured":"Lipski, C., Klose, F., Magnor, M.: Correspondence and depth-image based rendering a hybrid approach for free-viewpoint video. IEEE Trans. Circ. Syst. Video Technol. 24(6), 942\u2013951 (2014)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.cag.2023.10.002","volume":"116","author":"D Liu","year":"2023","unstructured":"Liu, D., Wan, W., Fang, Z., Zheng, X.: GsNeRF: fast novel view synthesis of dynamic radiance fields. Comput. Graph. 116, 491\u2013499 (2023)","journal-title":"Comput. Graph."},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Hedman, P., Martin-Brualla, R., Srinivasan, P.P., Barron, J.T.: NeRF in the dark: high dynamic range view synthesis from noisy raw images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16190\u201316199 (2022)","DOI":"10.1109\/CVPR52688.2022.01571"},{"issue":"1","key":"10_CR22","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"issue":"4","key":"10_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530127","volume":"41","author":"T M\u00fcller","year":"2022","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. (ToG) 41(4), 1\u201315 (2022)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: RegNeRF: regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5480\u20135490 (2022)","DOI":"10.1109\/CVPR52688.2022.00540"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Park, K., et al.: Nerfies: deformable neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5865\u20135874 (2021)","DOI":"10.1109\/ICCV48922.2021.00581"},{"key":"10_CR26","unstructured":"Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: DreamFusion: text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988 (2022)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-NeRF: neural radiance fields for dynamic scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10318\u201310327 (2021)","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Sun, C., Sun, M., Chen, H.T.: Direct voxel grid optimization: super-fast convergence for radiance fields reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5459\u20135469 (2022)","DOI":"10.1109\/CVPR52688.2022.00538"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Xu, Q., et al.: Point-NeRF: point-based neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5438\u20135448 (2022)","DOI":"10.1109\/CVPR52688.2022.00536"},{"issue":"12","key":"10_CR30","doi-asserted-by":"publisher","first-page":"4673","DOI":"10.1109\/TCSVT.2021.3100134","volume":"31","author":"P Zhang","year":"2021","unstructured":"Zhang, P., Wang, X., Ma, L., Wang, S., Kwong, S., Jiang, J.: Progressive point cloud upsampling via differentiable rendering. IEEE Trans. Circ. Syst. Video Technol. 31(12), 4673\u20134685 (2021)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, W., Xing, R., Zeng, Y., Liu, Y.S., Shi, K., Han, Z.: Fast learning radiance fields by shooting much fewer rays. IEEE Trans. Image Process. (2023)","DOI":"10.1109\/TIP.2023.3267049"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82021-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T09:45:45Z","timestamp":1740822345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82021-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031820205","9783031820212"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82021-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Geneva","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"41","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cgs-network.org\/cgi24\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}