Hash-NURF: Efficient Nested Transparent Object Reconstruction Using Multi-Resolution Hash Encoding

Hash-NURF: Efficient Nested Transparent Object Reconstruction Using Multi-Resolution Hash Encoding | 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 Hash-NURF: Efficient Nested Transparent Object Reconstruction Using Multi-Resolution Hash Encoding Fan Gao, Yibo Zhao, Youcheng Cai, Ligang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7805209/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted 9 You are reading this latest preprint version Abstract Transparent object reconstruction is crucial in various domains, including 3D modelling and robotic perception. However, accurately reconstructing nested transparent objects remains challenging due to complex light interactions. Recent advances like NU-NeRF have shown promise but suffer from computational inefficiency. Here, we introduce Hash-NURF, a framework that integrates multi-resolution hash encoding with neural implicit representations to accelerate reconstruction. Specifically, we utilize hash-based NeRF and SDF for efficient optimization. To unbounded scenes, we added a contractive mapping strategy. We also incorporated special sampling and regularization techniques to improve the model's stability and help it converge faster. Our method significantly reduces training time by up to three times while maintaining comparable reconstruction quality. We validate our approach on synthetic datasets, demonstrating its efficacy in both outer and inner surface reconstruction of nested transparent objects. Visit our repository at https://github.com/SyouSanGin/Hash-NURF Signed Distance Field (SDF) Transparent object Reconstruction Neural Implicit Representations Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 09 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 08 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. 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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-7805209","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539700077,"identity":"0c47fe92-f0e7-4566-83a4-8f597855634d","order_by":0,"name":"Fan Gao","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Gao","suffix":""},{"id":539700083,"identity":"2b8f3bf3-97f9-4c90-b39c-6dfd6e8da349","order_by":1,"name":"Yibo Zhao","email":"","orcid":"","institution":"University of Science and Technology of 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