{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T11:18:45Z","timestamp":1775128725210,"version":"3.50.1"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732317","type":"print"},{"value":"9783031732324","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"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\/978-3-031-73232-4_16","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:01:53Z","timestamp":1727589713000},"page":"283-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Radiative Gaussian Splatting for\u00a0Efficient X-Ray Novel View Synthesis"],"prefix":"10.1007","author":[{"given":"Yuanhao","family":"Cai","sequence":"first","affiliation":[]},{"given":"Yixun","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Angtian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yulun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zongwei","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Alan","family":"Yuille","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Andersen, A.H., Kak, A.C.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm. Ultrasonic imaging (1984)","DOI":"10.1177\/016173468400600107"},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"III Armato","year":"2011","unstructured":"Armato, I.I.I., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"16_CR3","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: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-NeRF 360: unbounded anti-aliased neural radiance fields. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-NeRF: anti-aliased grid-based neural radiance fields. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01804"},{"key":"16_CR6","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/2\/5\/055010","volume":"2","author":"A Biguri","year":"2016","unstructured":"Biguri, A., Dosanjh, M., Hancock, S., Soleimani, M.: TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction. Biomed. Phys. Eng. Express 2, 055010 (2016)","journal-title":"Biomed. Phys. Eng. Express"},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1118\/1.1636571","volume":"31","author":"J Boone","year":"2004","unstructured":"Boone, J., Shah, N., Nelson, T.: A comprehensive analysis of coefficients for pendant-geometry cone-beam breast computed tomography. Med. Phys. 31, 226\u2013235 (2004)","journal-title":"Med. Phys."},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1148\/radiol.2213010334","volume":"221","author":"JM Boone","year":"2001","unstructured":"Boone, J.M., Nelson, T.R., Lindfors, K.K., Seibert, J.A.: Dedicated breast CT: radiation dose and image quality evaluation. Radiology 221, 657\u2013667 (2001)","journal-title":"Radiology"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: TensoRF: tensorial radiance fields. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13692. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19824-3_20","DOI":"10.1007\/978-3-031-19824-3_20"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Chen, B., Ning, R.: Cone-beam volume CT breast imaging: Feasibility study. Med. Phys. 29, 755\u2013770 (2002)","DOI":"10.1118\/1.1461843"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Z., Funkhouser, T., Hedman, P., Tagliasacchi, A.: MobileNeRF: exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01590"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"2722","DOI":"10.1063\/1.1729798","volume":"34","author":"AM Cormack","year":"1963","unstructured":"Cormack, A.M.: Representation of a function by its line integrals, with some radiological applications. J. Appl. Phys. 34, 2722\u20132727 (1963)","journal-title":"J. Appl. Phys."},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"2908","DOI":"10.1063\/1.1713127","volume":"35","author":"AM Cormack","year":"1964","unstructured":"Cormack, A.M.: Representation of a function by its line integrals, with some radiological applications. II. J. Appl. Phys. 35, 2908\u20132913 (1964)","journal-title":"II. J. Appl. Phys."},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H.P., Willcocks, C.G.: MedNeRF: medical neural radiance fields for reconstructing 3D-aware CT-projections from a single x-ray. In: International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2022)","DOI":"10.1109\/EMBC48229.2022.9871757"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1088\/0031-9155\/48\/15\/314","volume":"48","author":"IA Elbakri","year":"2003","unstructured":"Elbakri, I.A., Fessler, J.A.: Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography with experimental validation. Phys. Med. Biol. 48, 2453 (2003)","journal-title":"Phys. Med. Biol."},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. Josa a 1, 612\u2013619 (1984)","DOI":"10.1364\/JOSAA.1.000612"},{"key":"16_CR17","unstructured":"Guide, D.: CUDA C programming guide. NVIDIA (2013)"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Hounsfield, G.N.: Computerized transverse axial scanning (tomography): Part 1. description of system. British J. Radiol. 46, 1016\u20131022 (1973)","DOI":"10.1259\/0007-1285-46-552-1016"},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1126\/science.6997993","volume":"210","author":"GN Hounsfield","year":"1980","unstructured":"Hounsfield, G.N.: Computed medical imaging. Science 210, 22\u201328 (1980)","journal-title":"Science"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Hu, S., Liu, Z.: GauHuman: articulated gaussian splatting from monocular human videos. arXiv preprint arXiv: (2023)","DOI":"10.1109\/CVPR52733.2024.01930"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Hu, T., Liu, S., Chen, Y., Shen, T., Jia, J.: EfficientNeRF efficient neural radiance fields. In: CVPR (2023)","DOI":"10.1109\/CVPR52688.2022.01256"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Hu, W., et al.: Tri-MipRF: Tri-Mip representation for efficient anti-aliasing neural radiance fields. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01811"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Jiang, Y., et al.: GaussianShader: 3D Gaussian splatting with shading functions for reflective surfaces. arXiv preprint arXiv:2311.17977 (2023)","DOI":"10.1109\/CVPR52733.2024.00509"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Keetha, N., et al.: Splatam: Splat, track & map 3D gaussians for dense RGB-D SLAM. arXiv preprint arXiv:2312.02126 (2023)","DOI":"10.1109\/CVPR52733.2024.02018"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42, 1\u201314 (2023)","DOI":"10.1145\/3592433"},{"key":"16_CR26","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"16_CR27","unstructured":"Klacansky, P.: Scientific visualization datasets (2022). https:\/\/klacansky.com\/open-scivis-datasets\/"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Chang, J.H.R., Gabriel, J., Tuzel, O., Ranjan, A.: Hugs: human gaussian splats. arXiv preprint arXiv:2311.17910 (2023)","DOI":"10.1109\/CVPR52733.2024.00055"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Kopanas, G., Philip, J., Leimk\u00fchler, T., Drettakis, G.: Point-based neural rendering with per-view optimization. In: Computer Graphics Forum (2021)","DOI":"10.1111\/cgf.14339"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Li, R., Gao, H., Tancik, M., Kanazawa, A.: NerfAcc: efficient sampling accelerates nerfs. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01699"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Liang, Y., Yang, X., Lin, J., Li, H., Xu, X., Chen, Y.: LucidDreamer: towards high-fidelity text-to-3D generation via interval score matching. arXiv preprint arXiv:2311.11284 (2023)","DOI":"10.1109\/CVPR52733.2024.00623"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Liang, Z., Zhang, Q., Feng, Y., Shan, Y., Jia, K.: GS-IR: 3D Gaussian splatting for inverse rendering. arXiv preprint arXiv:2311.16473 (2023)","DOI":"10.1109\/CVPR52733.2024.02045"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: HumanGaussian: Text-driven 3D human generation with gaussian splatting. arXiv preprint arXiv:2311.17061 (2023)","DOI":"10.1109\/CVPR52733.2024.00635"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Luiten, J., Kopanas, G., Leibe, B., Ramanan, D.: Dynamic 3D gaussians: tracking by persistent dynamic view synthesis. arXiv preprint arXiv:2308.09713 (2023)","DOI":"10.1109\/3DV62453.2024.00044"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Manglos, S.H., Gagne, G.M., Krol, A., Thomas, F.D., Narayanaswamy, R.: Transmission maximum-likelihood reconstruction with ordered subsets for cone beam ct. Phys. Med. Biol. 40, 1225 (1995)","DOI":"10.1088\/0031-9155\/40\/7\/006"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Matsuki, H., Murai, R., Kelly, P.H., Davison, A.J.: Gaussian splatting slam. arXiv preprint arXiv:2312.06741 (2023)","DOI":"10.1109\/CVPR52733.2024.01708"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P., Tancik, M., Barron, J., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM ToG (2022)","DOI":"10.1145\/3528223.3530127"},{"key":"16_CR39","doi-asserted-by":"publisher","DOI":"10.1155\/IJBI\/2006\/10398","volume":"2006","author":"J Pan","year":"2006","unstructured":"Pan, J., Zhou, T., Han, Y., Jiang, M., et al.: Variable weighted ordered subset image reconstruction algorithm. Int. J. Biomed. Imaging 2006, 010398 (2006)","journal-title":"Int. J. Biomed. Imaging"},{"key":"16_CR40","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Reiser, C., et al.: MERF: memory-efficient radiance fields for real-time view synthesis in unbounded scenes. TOG (2023)","DOI":"10.1145\/3592426"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"R\u00fcckert, D., Wang, Y., Li, R., Idoughi, R., Heidrich, W.: NeAT: neural adaptive tomography. TOG (2022)","DOI":"10.1145\/3528223.3530121"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Sauer, K., Bouman, C.: A local update strategy for iterative reconstruction from projections. TIP (1993)","DOI":"10.1109\/78.193196"},{"key":"16_CR44","unstructured":"Scarfe, W.C., Farman, A.G., Sukovic, P., et\u00a0al.: Clinical applications of cone-beam computed tomography in dental practice. J. Can. Dent. Assoc. 72, 75\u201380 (2006)"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 53, 4777\u20134807 (2008)","DOI":"10.1088\/0031-9155\/53\/17\/021"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: SIGGRAPH (2006)","DOI":"10.1145\/1141911.1141964"},{"key":"16_CR47","unstructured":"Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: DreamGaussian: generative gaussian splatting for efficient 3D content creation. arXiv preprint arXiv:2309.16653 (2023)"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: structured view-dependent appearance for neural radiance fields. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00541"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncell, E.P.: Image quality assessment: from error visibility to structural similarity. TIP (2004)","DOI":"10.1109\/TIP.2003.819861"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Wu, G., et al.: 4D Gaussian splatting for real-time dynamic scene rendering. arXiv preprint arXiv:2310.08528 (2023)","DOI":"10.1109\/CVPR52733.2024.01920"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Xie, T., et al.: PhysGaussian: physics-integrated 3D gaussians for generative dynamics. arXiv preprint arXiv:2311.12198 (2023)","DOI":"10.1109\/CVPR52733.2024.00420"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Yan, C., Qu, D., Wang, D., Xu, D., Wang, Z., Zhao, B., Li, X.: GS-SLAM: dense visual slam with 3D gaussian splatting. arXiv preprint arXiv:2311.11700 (2023)","DOI":"10.1109\/CVPR52733.2024.01853"},{"key":"16_CR53","unstructured":"Yang, Z., Yang, H., Pan, Z., Zhu, X., Zhang, L.: Real-time photorealistic dynamic scene representation and rendering with 4D gaussian splatting. arXiv preprint arXiv:2310.10642 (2023)"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Yariv, L., et al.: BakedSDF: meshing neural SDFs for real-time view synthesis. In: SIGGRAPH (2023)","DOI":"10.1145\/3588432.3591536"},{"key":"16_CR55","unstructured":"Yi, T., et al.: GaussianDreamer: fast generation from text to 3D Gaussian splatting with point cloud priors. arXiv preprint arXiv:2310.08529 (2023)"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Yu, L., Zou, Y., Sidky, E.Y., Pelizzari, C.A., Munro, P., Pan, X.: Region of interest reconstruction from truncated data in circular cone-beam CT. TMI (2006)","DOI":"10.1117\/12.595893"},{"key":"16_CR57","unstructured":"Yugay, V., Li, Y., Gevers, T., Oswald, M.R.: Gaussian-SLAM: photo-realistic dense slam with gaussian splatting. arXiv preprint arXiv:2312.10070 (2023)"},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"Zang, G., Idoughi, R., Li, R., Wonka, P., Heidrich, W.: IntraTomo: self-supervised learning-based tomography via sinogram synthesis and prediction. In: CVPR (2021)","DOI":"10.1109\/ICCV48922.2021.00197"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Zbijewski, W., Defrise, M., Viergever, M.A., Beekman, F.J.: Statistical reconstruction for x-ray CT systems with non-continuous detectors. Phys. Med. Biol. 52, 403 (2006)","DOI":"10.1088\/0031-9155\/52\/2\/007"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Zha, R., Zhang, Y., Li, H.: NAF: neural attenuation fields for sparse-view CBCT reconstruction. In: MICCAI (2022)","DOI":"10.1007\/978-3-031-16446-0_42"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: PhysDreamer: physics-based interaction with 3d objects via video generation. In: ECCV (2024)","DOI":"10.1007\/978-3-031-72627-9_22"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Zwicker, M., Pfister, H., Van\u00a0Baar, J., Gross, M.: EWA volume splatting. In: Proceedings Visualization, 2001. VIS 2001. IEEE (2001)","DOI":"10.1145\/383259.383300"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73232-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T21:14:08Z","timestamp":1732828448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73232-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031732317","9783031732324"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73232-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}