NDDR: Neural Dual-Domain Representation for High-Fidelity Dental Mesh Compression

NDDR: Neural Dual-Domain Representation for High-Fidelity Dental Mesh Compression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article NDDR: Neural Dual-Domain Representation for High-Fidelity Dental Mesh Compression Yuteng Liu, Yutong Hu, Haisheng Li, Li Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7989025/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted 9 You are reading this latest preprint version Abstract In dental clinical practice, the use of digital three-dimensional surface models is increasingly prevalent. However, high-resolution dental meshes pose significant storage and computational challenges. This paper introduces a Dual-Domain Neural Representation (NDDR) framework for dental mesh compression, leveraging geometric simplification and neural networks to achieve high compression ratios while preserving critical dental features. The proposed method first generates a coarse mesh representation, then predicts fine-grained displacements through a neural network, and finally optimizes both the visual and geometric domains to obtain a high-fidelity and visually coherent reconstruction. Experimental results demonstrate superior performance in detail preservation and compression efficiency compared to traditional and learning-based methods, achieving a 99% reduction in file size with minimal loss of geometric detail. Code is available at: https://github.com/CCShermit1/NDDR/tree/master Neural representation Mesh compression Mesh upsampling Coarse to fine Digital dentistry Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 14 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers invited by journal 16 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 30 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7989025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546761396,"identity":"af55a0d4-2493-44b2-92ce-e9609f6969cf","order_by":0,"name":"Yuteng Liu","email":"","orcid":"","institution":"Beijing Technology and Business university","correspondingAuthor":false,"prefix":"","firstName":"Yuteng","middleName":"","lastName":"Liu","suffix":""},{"id":546761398,"identity":"44ba4c5d-acd3-4b74-a955-a844fe3b1dc2","order_by":1,"name":"Yutong Hu","email":"","orcid":"","institution":"Beijing Technology and Business 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