{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:57:39Z","timestamp":1782136659324,"version":"3.54.5"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDA0320402"],"award-info":[{"award-number":["XDA0320402"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Graphics"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.cag.2026.104596","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T23:14:28Z","timestamp":1776467668000},"page":"104596","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["QuantizationGS: Differentiable quantization model for 3D Gaussian Splatting compression"],"prefix":"10.1016","volume":"137","author":[{"given":"Yiming","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanlin","family":"Mou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daobing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cag.2026.104596_b1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"Nerf: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun ACM"},{"key":"10.1016\/j.cag.2026.104596_b2","series-title":"Geomatic approaches for modeling land change scenarios","first-page":"451","article-title":"Multilayer perceptron (MLP)","author":"Taud","year":"2017"},{"issue":"4","key":"10.1016\/j.cag.2026.104596_b3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3592433","article-title":"3D Gaussian splatting for real-time radiance field rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cag.2026.104596_b4","series-title":"European conference on computer vision","first-page":"333","article-title":"Tensorf: Tensorial radiance fields","author":"Chen","year":"2022"},{"key":"10.1016\/j.cag.2026.104596_b5","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil S, Meanti G, Warburg FR, Recht B, Kanazawa A. K-planes: Explicit radiance fields in space, time, and appearance. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 12479\u201388.","DOI":"10.1109\/CVPR52729.2023.01201"},{"key":"10.1016\/j.cag.2026.104596_b6","doi-asserted-by":"crossref","unstructured":"Gao Q, Xu Q, Su H, Neumann U, Xu Z. Strivec: Sparse tri-vector radiance fields. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2023, p. 17569\u201379.","DOI":"10.1109\/ICCV51070.2023.01611"},{"key":"10.1016\/j.cag.2026.104596_b7","doi-asserted-by":"crossref","unstructured":"Han K, Xiang W. Multiscale tensor decomposition and rendering equation encoding for view synthesis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 4232\u201341.","DOI":"10.1109\/CVPR52729.2023.00412"},{"issue":"4","key":"10.1016\/j.cag.2026.104596_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3528223.3530127","article-title":"Instant neural graphics primitives with a multiresolution hash encoding","volume":"41","author":"M\u00fcller","year":"2022","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cag.2026.104596_b9","doi-asserted-by":"crossref","unstructured":"Girish S, Shrivastava A, Gupta K. Shacira: Scalable hash-grid compression for implicit neural representations. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2023, p. 17513\u201324.","DOI":"10.1109\/ICCV51070.2023.01606"},{"key":"10.1016\/j.cag.2026.104596_b10","doi-asserted-by":"crossref","unstructured":"Schonberger JL, Frahm J-M. Structure-from-motion revisited. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 4104\u201313.","DOI":"10.1109\/CVPR.2016.445"},{"key":"10.1016\/j.cag.2026.104596_b11","series-title":"European conference on computer vision","first-page":"106","article-title":"Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering","author":"Xiangli","year":"2022"},{"issue":"4","key":"10.1016\/j.cag.2026.104596_b12","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s41095-024-0436-y","article-title":"Recent advances in 3d gaussian splatting","volume":"10","author":"Wu","year":"2024","journal-title":"Comput Vis Media"},{"key":"10.1016\/j.cag.2026.104596_b13","article-title":"3D gaussian splatting as new era: A survey","author":"Fei","year":"2024","journal-title":"IEEE Trans Vis Comput Graphics"},{"key":"10.1016\/j.cag.2026.104596_b14","series-title":"Computer graphics forum","article-title":"3Dgs. zip: A survey on 3d gaussian splatting compression methods","volume":"vol. 44","author":"Bagdasarian","year":"2025"},{"key":"10.1016\/j.cag.2026.104596_b15","doi-asserted-by":"crossref","first-page":"140138","DOI":"10.52202\/079017-4447","article-title":"Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps","volume":"37","author":"Fan","year":"2024","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.cag.2026.104596_b16","doi-asserted-by":"crossref","unstructured":"Lee JC, Rho D, Sun X, Ko JH, Park E. Compact 3d gaussian representation for radiance field. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024, p. 21719\u201328.","DOI":"10.1109\/CVPR52733.2024.02052"},{"key":"10.1016\/j.cag.2026.104596_b17","series-title":"European conference on computer vision","first-page":"330","article-title":"Compgs: Smaller and faster gaussian splatting with vector quantization","author":"Navaneet","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b18","doi-asserted-by":"crossref","unstructured":"Niedermayr S, Stumpfegger J, Westermann R. Compressed 3d gaussian splatting for accelerated novel view synthesis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024, p. 10349\u201358.","DOI":"10.1109\/CVPR52733.2024.00985"},{"key":"10.1016\/j.cag.2026.104596_b19","series-title":"European conference on computer vision","first-page":"54","article-title":"Eagles: Efficient accelerated 3d gaussians with lightweight encodings","author":"Girish","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b20","series-title":"European conference on computer vision","first-page":"422","article-title":"Hac: Hash-grid assisted context for 3d gaussian splatting compression","author":"Chen","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b21","series-title":"European conference on computer vision","first-page":"18","article-title":"Compact 3d scene representation via self-organizing gaussian grids","author":"Morgenstern","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b22","doi-asserted-by":"crossref","unstructured":"Lu T, Yu M, Xu L, Xiangli Y, Wang L, Lin D, Dai B. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024, p. 20654\u201364.","DOI":"10.1109\/CVPR52733.2024.01952"},{"key":"10.1016\/j.cag.2026.104596_b23","series-title":"Differentiable model compression via pseudo quantization noise","author":"D\u00e9fossez","year":"2021"},{"key":"10.1016\/j.cag.2026.104596_b24","doi-asserted-by":"crossref","unstructured":"Barron JT, Mildenhall B, Tancik M, Hedman P, Martin-Brualla R, Srinivasan PP. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2021, p. 5855\u201364.","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"10.1016\/j.cag.2026.104596_b25","doi-asserted-by":"crossref","unstructured":"Rho D, Lee B, Nam S, Lee JC, Ko JH, Park E. Masked wavelet representation for compact neural radiance fields. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 20680\u201390.","DOI":"10.1109\/CVPR52729.2023.01981"},{"key":"10.1016\/j.cag.2026.104596_b26","doi-asserted-by":"crossref","unstructured":"Takikawa T, Evans A, Tremblay J, M\u00fcller T, McGuire M, Jacobson A, Fidler S. Variable bitrate neural fields. In: ACM SIGGRAPH 2022 conference proceedings. 2022, p. 1\u20139.","DOI":"10.1145\/3528233.3530727"},{"key":"10.1016\/j.cag.2026.104596_b27","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. 2022, p. 5459\u201369.","DOI":"10.1109\/CVPR52688.2022.00538"},{"key":"10.1016\/j.cag.2026.104596_b28","doi-asserted-by":"crossref","unstructured":"Li L, Shen Z, Wang Z, Shen L, Bo L. Compressing volumetric radiance fields to 1 mb. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 4222\u201331.","DOI":"10.1109\/CVPR52729.2023.00411"},{"key":"10.1016\/j.cag.2026.104596_b29","doi-asserted-by":"crossref","unstructured":"Deng CL, Tartaglione E. Compressing explicit voxel grid representations: fast nerfs become also small. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. 2023, p. 1236\u201345.","DOI":"10.1109\/WACV56688.2023.00129"},{"key":"10.1016\/j.cag.2026.104596_b30","series-title":"2023 IEEE international conference on multimedia and expo","first-page":"2189","article-title":"Compact real-time radiance fields with neural codebook","author":"Li","year":"2023"},{"key":"10.1016\/j.cag.2026.104596_b31","doi-asserted-by":"crossref","first-page":"55919","DOI":"10.52202\/075280-2438","article-title":"Binary radiance fields","volume":"36","author":"Shin","year":"2023","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.cag.2026.104596_b32","doi-asserted-by":"crossref","first-page":"14798","DOI":"10.52202\/068431-1076","article-title":"Compressible-composable nerf via rank-residual decomposition","volume":"35","author":"Tang","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.cag.2026.104596_b33","series-title":"Spc-nerf: spatial predictive compression for voxel based radiance field","author":"Song","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b34","doi-asserted-by":"crossref","unstructured":"Chen Y, Wu Q, Harandi M, Cai J. How far can we compress instant-ngp-based nerf?. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024, p. 20321\u201330.","DOI":"10.1109\/CVPR52733.2024.01921"},{"key":"10.1016\/j.cag.2026.104596_b35","doi-asserted-by":"crossref","unstructured":"Li S, Li H, Liao Y, Yu L. Nerfcodec: Neural feature compression meets neural radiance fields for memory-efficient scene representation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024, p. 21274\u201383.","DOI":"10.1109\/CVPR52733.2024.02010"},{"key":"10.1016\/j.cag.2026.104596_b36","doi-asserted-by":"crossref","unstructured":"Kang G, Lee Y, Oh S, Park E. Codecnerf: Toward fast encoding and decoding, compact, and high-quality novel-view synthesis. In: Proceedings of the AAAI conference on artificial intelligence. vol. 39, 2025, p. 4203\u201311.","DOI":"10.1609\/aaai.v39i4.32441"},{"key":"10.1016\/j.cag.2026.104596_b37","doi-asserted-by":"crossref","unstructured":"Lassner C, Zollhofer M. Pulsar: Efficient sphere-based neural rendering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021, p. 1440\u20139.","DOI":"10.1109\/CVPR46437.2021.00149"},{"issue":"1","key":"10.1016\/j.cag.2026.104596_b38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3651282","article-title":"Reducing the memory footprint of 3d gaussian splatting","volume":"7","author":"Papantonakis","year":"2024","journal-title":"Proc ACM Comput Graph Interact Tech"},{"key":"10.1016\/j.cag.2026.104596_b39","series-title":"Trimming the fat: Efficient compression of 3d gaussian splats through pruning","author":"Ali","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b40","series-title":"European conference on computer vision","first-page":"76","article-title":"End-to-end rate-distortion optimized 3d gaussian representation","author":"Wang","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b41","series-title":"Spectrally pruned gaussian fields with neural compensation","author":"Yang","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b42","series-title":"European conference on computer vision","first-page":"165","article-title":"Mini-splatting: Representing scenes with a constrained number of gaussians","author":"Fang","year":"2024"},{"key":"10.1016\/j.cag.2026.104596_b43","series-title":"Implicit gaussian splatting with efficient multi-level tri-plane representation","author":"Wu","year":"2024"},{"issue":"3","key":"10.1016\/j.cag.2026.104596_b44","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/3477.764879","article-title":"Genetic K-means algorithm","volume":"29","author":"Krishna","year":"1999","journal-title":"IEEE Trans Syst Man Cybern B"},{"issue":"2","key":"10.1016\/j.cag.2026.104596_b45","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/19.492748","article-title":"Statistical theory of quantization","volume":"45","author":"Widrow","year":"1996","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.cag.2026.104596_b46","doi-asserted-by":"crossref","unstructured":"Polychroniou O, Ross KA. Efficient lightweight compression alongside fast scans. In: Proceedings of the 11th international workshop on data management on new hardware. 2015, p. 1\u20136.","DOI":"10.1145\/2771937.2771943"},{"key":"10.1016\/j.cag.2026.104596_b47","series-title":"Zlib compression library","author":"Gailly","year":"2004"},{"key":"10.1016\/j.cag.2026.104596_b48","series-title":"History and definition of the sobel operator","author":"Sobel","year":"2014"},{"key":"10.1016\/j.cag.2026.104596_b49","doi-asserted-by":"crossref","unstructured":"Barron JT, Mildenhall B, Verbin D, Srinivasan PP, Hedman P. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 5470\u20139.","DOI":"10.1109\/CVPR52688.2022.00539"},{"issue":"4","key":"10.1016\/j.cag.2026.104596_b50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073599","article-title":"Tanks and temples: Benchmarking large-scale scene reconstruction","volume":"36","author":"Knapitsch","year":"2017","journal-title":"ACM Trans Graph (ToG)"},{"issue":"6","key":"10.1016\/j.cag.2026.104596_b51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3272127.3275084","article-title":"Deep blending for free-viewpoint image-based rendering","volume":"37","author":"Hedman","year":"2018","journal-title":"ACM Trans Graph (ToG)"},{"key":"10.1016\/j.cag.2026.104596_b52","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 586\u201395.","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Computers & Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0097849326000671?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0097849326000671?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:14:39Z","timestamp":1782134079000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0097849326000671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":52,"alternative-id":["S0097849326000671"],"URL":"https:\/\/doi.org\/10.1016\/j.cag.2026.104596","relation":{},"ISSN":["0097-8493"],"issn-type":[{"value":"0097-8493","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"QuantizationGS: Differentiable quantization model for 3D Gaussian Splatting compression","name":"articletitle","label":"Article Title"},{"value":"Computers & Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cag.2026.104596","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104596"}}