Real-Time and High-Fidelity Non-Line-of-Sight Imaging

Real-Time and High-Fidelity Non-Line-of-Sight Imaging | 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 Article Real-Time and High-Fidelity Non-Line-of-Sight Imaging Xiangyang Ji, Jianyu Wang, Leping Xiao, Shiwei Wu, Yuran Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8336286/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-line-of-sight (NLOS) imaging, which aims to reconstruct objects hidden from direct view, including see-through-the-medium and see-around-the-corner scenario categories, has become a promising field with broad applications. In this work, we introduce a unified NLOS reconstruction framework that addresses both categories of NLOS imaging problems. By incorporating scale modulation and joint regularization terms, the framework efficiently recovers albedo and depth across diverse measurement settings while enhancing reconstruction quality. To the best of our knowledge, this is the first method to deliver high-fidelity reconstruction with high computational efficiency across general NLOS imaging scenarios, providing a practical solution to real-world challenges. Moreover, we introduce a novel dataset that covers multiple measurement settings for both scenario categories, supporting future research in the field. Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Mathematics and computing/Computer science Non-line-of-sight imaging Inverse problem Regularization Real-time reconstruction High-fidelity reconstruction Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supp.pdf Real-Time and High-Fidelity Non-Line-of-Sight Imaging Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8336286","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601191424,"identity":"43986b69-d77c-4a87-bc91-ca65788cf352","order_by":0,"name":"Xiangyang 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