{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:47:41Z","timestamp":1780512461935,"version":"3.54.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["10108734"],"award-info":[{"award-number":["10108734"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["101097259"],"award-info":[{"award-number":["101097259"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["788065"],"award-info":[{"award-number":["788065"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["101141721"],"award-info":[{"award-number":["101141721"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>\n                    While recent advances in Gaussian Splatting have enabled fast reconstruction of high-quality 3D scenes from images, extracting accurate surface meshes remains a challenge. Current approaches extract the surface through costly post-processing steps, resulting in the loss of fine geometric details or requiring significant time and leading to very dense meshes with millions of vertices. More fundamentally, the\n                    <jats:italic toggle=\"yes\">a posteriori<\/jats:italic>\n                    conversion from a volumetric to a surface representation limits the ability of the final mesh to preserve all geometric structures captured during training. We present MILo, a novel Gaussian Splatting framework that bridges the gap between volumetric and surface representations by differentiably extracting a mesh from the 3D Gaussians. We design a fully differentiable procedure that constructs the mesh\u2014including both vertex locations and connectivity\u2014at every iteration directly from the parameters of the Gaussians,\n                    <jats:italic toggle=\"yes\">which are the only quantities optimized during training.<\/jats:italic>\n                  <\/jats:p>\n                  <jats:p>Our method introduces three key technical contributions: (1) a bidirectional consistency framework ensuring both representations\u2014Gaussians and the extracted mesh\u2014capture the same underlying geometry during training; (2) an adaptive mesh extraction process performed at each training iteration, which uses Gaussians as differentiable pivots for Delaunay triangulation; (3) a novel method for computing signed distance values from the 3D Gaussians that enables precise surface extraction while avoiding geometric erosion.<\/jats:p>\n                  <jats:p>Our approach can reconstruct complete scenes, including backgrounds, with state-of-the-art quality while requiring an order of magnitude fewer mesh vertices than previous methods.<\/jats:p>\n                  <jats:p>Due to their light weight and empty interior, our meshes are well suited for downstream applications such as physics simulations and animation.<\/jats:p>\n                  <jats:p>The code for our approach and an online gallery are available at https:\/\/anttwo.github.io\/milo\/.<\/jats:p>","DOI":"10.1145\/3763339","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T17:15:39Z","timestamp":1764868539000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["MILo: Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstruction"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3107-4454","authenticated-orcid":false,"given":"Antoine","family":"Gu\u00e9don","sequence":"first","affiliation":[{"name":"\u00c9cole Polytechnique, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2847-8617","authenticated-orcid":false,"given":"Diego","family":"Gomez","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0866-7058","authenticated-orcid":false,"given":"Nissim","family":"Maruani","sequence":"additional","affiliation":[{"name":"Institut national de recherche en informatique et en automatique (INRIA), Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6459-6972","authenticated-orcid":false,"given":"Bingchen","family":"Gong","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9254-4819","authenticated-orcid":false,"given":"George","family":"Drettakis","sequence":"additional","affiliation":[{"name":"Institut national de recherche en informatique et en automatique (INRIA), Sophia Antipolis, France"},{"name":"Universit\u00e9 Cote d'Azur, Sophia Antipolis, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-4046","authenticated-orcid":false,"given":"Maks","family":"Ovsjanikov","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/280814.280947"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/376957.376986"},{"key":"e_1_2_1_3_1","volume-title":"Voronoi diagrams\u2014a survey of a fundamental geometric data structure. ACM computing surveys (CSUR) 23, 3","author":"Aurenhammer Franz","year":"1991","unstructured":"Franz Aurenhammer. 1991. Voronoi diagrams\u2014a survey of a fundamental geometric data structure. ACM computing surveys (CSUR) 23, 3 (1991), 345\u2013405."},{"key":"e_1_2_1_4_1","volume-title":"Ricardo Martin Brualla, and Pratul P Srinivasan","author":"Barron Jonathan T","year":"2021","unstructured":"Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin Brualla, and Pratul P Srinivasan. 2021. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. In ICCV. 5855\u20135864."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Jonathan T. Barron Ben Mildenhall Dor Verbin Pratul P. Srinivasan and Peter Hedman. 2023. Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields. In ICCV. 19697\u201319705.","DOI":"10.1109\/ICCV51070.2023.01804"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Anpei Chen Zexiang Xu Andreas Geiger Jingyi Yu and Hao Su. 2022. TensoRF: Tensorial Radiance Fields. In ECCV. 333\u2013350.","DOI":"10.1007\/978-3-031-19824-3_20"},{"key":"e_1_2_1_8_1","volume-title":"NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance. arXiv preprint arXiv:2312.00846","author":"Chen Hanlin","year":"2023","unstructured":"Hanlin Chen, Chen Li, and Gim Hee Lee. 2023. NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance. arXiv preprint arXiv:2312.00846 (2023)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Hanlin Chen Fangyin Wei Chen Li Tianxin Huang Yunsong Wang and Gim Hee Lee. 2024. VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction. In NeurIPS.","DOI":"10.52202\/079017-4434"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3641519.3657441"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/781606.781627"},{"key":"e_1_2_1_12_1","first-page":"214","article-title":"An efficient method of triangulating equi-valued surfaces by using tetrahedral cells","volume":"74","author":"Doi Akio","year":"1991","unstructured":"Akio Doi and Akio Koide. 1991. An efficient method of triangulating equi-valued surfaces by using tetrahedral cells. IEICE TRANSACTIONS on Information and Systems 74, 1 (1991), 214\u2013224.","journal-title":"IEICE TRANSACTIONS on Information and Systems"},{"key":"e_1_2_1_13_1","unstructured":"Tim Elsner Victor Czech Julia Berger Zain Selman Isaak Lim and Leif Kobbelt. 2023. Adaptive Voronoi NeRFs. arXiv:2303.16001 [cs] http:\/\/arxiv.org\/abs\/2303.16001"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72980-5_10"},{"key":"e_1_2_1_15_1","volume-title":"Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification. arXiv preprint arXiv:2411.12788","author":"Fang Guangchi","year":"2024","unstructured":"Guangchi Fang and Bing Wang. 2024b. Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification. arXiv preprint arXiv:2411.12788 (2024)."},{"key":"e_1_2_1_16_1","volume-title":"Plenoxels: Radiance Fields without Neural Networks. In CVPR. 5501\u20135510.","author":"Fridovich-Keil Sara","year":"2022","unstructured":"Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. 2022. Plenoxels: Radiance Fields without Neural Networks. In CVPR. 5501\u20135510."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV66043.2025.00061"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2502.01157"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Antoine Gu\u00e9don Tomoki Ichikawa Kohei Yamashita and Ko Nishino. 2025. MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views. In CVPR.","DOI":"10.1109\/CVPR52734.2025.00563"},{"key":"e_1_2_1_20_1","volume-title":"Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering. In ECCV.","author":"Gu\u00e9don Antoine","year":"2024","unstructured":"Antoine Gu\u00e9don and Vincent Lepetit. 2024a. Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering. In ECCV."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Antoine Gu\u00e9don and Vincent Lepetit. 2024b. SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering. In CVPR. 5354\u20135363.","DOI":"10.1109\/CVPR52733.2024.00512"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275084"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3641519.3657428"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3641519.3657428"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.59"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_2_1_27_1","first-page":"80965","article-title":"3d gaussian splatting as markov chain monte carlo","volume":"37","author":"Kheradmand Shakiba","year":"2024","unstructured":"Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Yang-Che Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 2024. 3d gaussian splatting as markov chain monte carlo. Advances in Neural Information Processing Systems 37 (2024), 80965\u201380986.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073599"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073599"},{"key":"e_1_2_1_30_1","volume-title":"Modular Primitives for High-Performance Differentiable Rendering. ACM TOG 39, 6","author":"Laine Samuli","year":"2020","unstructured":"Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. 2020. Modular Primitives for High-Performance Differentiable Rendering. ACM TOG 39, 6 (2020)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02525"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00817"},{"key":"e_1_2_1_33_1","volume-title":"Deformable Beta Splatting. arXiv preprint arXiv:2501.18630","author":"Liu Rong","year":"2025","unstructured":"Rong Liu, Dylan Sun, Meida Chen, Yue Wang, and Andrew Feng. 2025. Deformable Beta Splatting. arXiv preprint arXiv:2501.18630 (2025)."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3680528.3687694"},{"key":"e_1_2_1_35_1","volume-title":"VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams. In 2023 IEEE\/CVF International Conference on Computer Vision (ICCV). 14565\u201314574","author":"Maruani Nissim","year":"2023","unstructured":"Nissim Maruani, Roman Klokov, Maks Ovsjanikov, Pierre Alliez, and Mathieu Desbrun. 2023. VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams. In 2023 IEEE\/CVF International Conference on Computer Vision (ICCV). 14565\u201314574. https:\/\/openaccess.thecvf.com\/content\/ICCV2023\/html\/Maruani_VoroMesh_Learning_Watertight_Surface_Meshes_with_Voronoi_Diagrams_ICCV_2023_paper.html"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00350"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Ben Mildenhall Pratul P. Srinivasan Matthew Tancik Jonathan T. Barron Ravi Ramamoorthi and Ren Ng. 2020. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In ECCV. 99\u2013106.","DOI":"10.1145\/3503250"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3687934"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530127"},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"J. Munkberg J. Hasselgren T. Shen J. Gao W. Chen A. Evans T. M\u00fcller and S. Fidler. 2022. Extracting Triangular 3D Models Materials and Lighting From Images. In CVPR.","DOI":"10.1109\/CVPR52688.2022.00810"},{"key":"e_1_2_1_41_1","volume-title":"Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis. arXiv preprint arXiv:2402.12377","author":"Reiser Christian","year":"2024","unstructured":"Christian Reiser, Stephan Garbin, Pratul P Srinivasan, Dor Verbin, Richard Szeliski, Ben Mildenhall, Jonathan T Barron, Peter Hedman, and Andreas Geiger. 2024a. Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis. arXiv preprint arXiv:2402.12377 (2024)."},{"key":"e_1_2_1_42_1","volume-title":"Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis. SIGGRAPH","author":"Reiser Christian","year":"2024","unstructured":"Christian Reiser, Stephan Garbin, Pratul P. Srinivasan, Dor Verbin, Richard Szeliski, Ben Mildenhall, Jonathan T. Barron, Peter Hedman, and Andreas Geiger. 2024b. Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis. SIGGRAPH (2024)."},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","unstructured":"T. Shen J. Munkberg J. Hasselgren K. Yin Z. Wang W. Chen Z. Gojcic S. Fidler N. Sharp and J. Gao. 2023. Flexible Isosurface Extraction for Gradient-Based Mesh Optimization. ACM TOG 42 4 (2023).","DOI":"10.1145\/3592430"},{"key":"e_1_2_1_44_1","volume-title":"Srinivasan","author":"Verbin Dor","year":"2022","unstructured":"Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, and Pratul P. Srinivasan. 2022. Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields. CVPR (2022)."},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems (NIPS). Article","author":"Wang Peng","year":"2021","unstructured":"Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. 2021. NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Proceedings of the 35th International Conference on Neural Information Processing Systems (NIPS). Article 2081, 13 pages."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00305"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01661"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00140"},{"key":"e_1_2_1_49_1","volume-title":"Ashkan Mirzaei, Nicolas Moenne-Loccoz, and Zan Gojcic.","author":"Wu Qi","year":"2024","unstructured":"Qi Wu, Janick Martinez Esturo, Ashkan Mirzaei, Nicolas Moenne-Loccoz, and Zan Gojcic. 2024. 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting. arXiv preprint arXiv:2412.12507 (2024)."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00798"},{"key":"e_1_2_1_51_1","unstructured":"Lior Yariv Jiatao Gu Yoni Kasten and Yaron Lipman. 2021. Volume rendering of neural implicit surfaces. (2021)."},{"key":"e_1_2_1_52_1","volume-title":"BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis. ACM SIGGRAPH Conference Papers","author":"Yariv L.","year":"2023","unstructured":"L. Yariv, P. Hedman, C. Reiser, D. Verbin, P. P. Srinivasan, R. Szeliski, J. T. Barron, and B. Mildenhall. 2023. BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis. ACM SIGGRAPH Conference Papers (2023)."},{"key":"e_1_2_1_53_1","unstructured":"Mulin Yu Tao Lu Linning Xu Lihan Jiang Yuanbo Xiangli and Bo Dai. 2024b. GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction. In NeurIPS."},{"key":"e_1_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Zehao Yu Anpei Chen Binbin Huang Torsten Sattler and Andreas Geiger. 2024a. Mip-Splatting: Alias-free 3D Gaussian Splatting. In CVPR. 19447\u201319456.","DOI":"10.1109\/CVPR52733.2024.01839"},{"key":"e_1_2_1_55_1","volume-title":"Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes. ACM Transactions on Graphics","author":"Yu Zehao","year":"2024","unstructured":"Zehao Yu, Torsten Sattler, and Andreas Geiger. 2024. Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes. ACM Transactions on Graphics (2024)."},{"key":"e_1_2_1_56_1","volume-title":"Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes. ACM TOG","author":"Yu Zehao","year":"2024","unstructured":"Zehao Yu, Torsten Sattler, and Andreas Geiger. 2024. Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes. ACM TOG (2024)."},{"key":"e_1_2_1_57_1","volume-title":"RaDe-GS: Rasterizing Depth in Gaussian Splatting. arXiv preprint arXiv:2406.01467","author":"Zhang Baowen","year":"2024","unstructured":"Baowen Zhang, Chuan Fang, Rakesh Shrestha, Yixun Liang, Xiaoxiao Long, and Ping Tan. 2024. RaDe-GS: Rasterizing Depth in Gaussian Splatting. arXiv preprint arXiv:2406.01467 (2024)."},{"key":"e_1_2_1_58_1","unstructured":"Wenyuan Zhang Yu-Shen Liu and Zhizhong Han. 2024b. Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_1_59_1","volume-title":"Quadratic Gaussian Splatting for Efficient and Detailed Surface Reconstruction. arXiv preprint arXiv:2411.16392","author":"Zhang Ziyu","year":"2024","unstructured":"Ziyu Zhang, Binbin Huang, Hanqing Jiang, Liyang Zhou, Xiaojun Xiang, and Shunhan Shen. 2024a. Quadratic Gaussian Splatting for Efficient and Detailed Surface Reconstruction. arXiv preprint arXiv:2411.16392 (2024)."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3763339","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T21:11:17Z","timestamp":1764969077000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3763339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":59,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1145\/3763339"],"URL":"https:\/\/doi.org\/10.1145\/3763339","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2025-05-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}