A Multi-Source Fusion 3D Reconstruction Method for Laser Demolition Rescue Environments

A Multi-Source Fusion 3D Reconstruction Method for Laser Demolition Rescue Environments | 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 A Multi-Source Fusion 3D Reconstruction Method for Laser Demolition Rescue Environments Yi CHEN, Dewen TANG, Xuelin DU, Mingxiao TENG, Zhanyu WU, Weilin ZENG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8596378/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This paper proposes a multi-source fusion based 3D reconstruction method to address the insufficient real time performance and low reliability of scene modeling in laser demolition rescue scenarios, where complex environmental factors such as structural collapse, smoke occlusion, and dense dynamic obstacles are common. First, spatiotemporal synchronization and calibration are performed on LiDAR, infrared camera, and IMU sensors to ensure data consistency. Second, a multimodal feature extraction and fusion network is designed to integrate complementary sensor data at the feature level, incorporating an attention mechanism to enhance informative features and suppress noise. Finally, using a real-time optimized reconstruction algorithm, the fused data enable accurate, complete, and dynamic 3D reconstruction of the rescue scene, supporting reliable decision-making and path planning. Experimental results show that the proposed method outperforms single-sensor approaches across key metrics. The reconstruction RMSE is reduced to 11.233, representing reductions of 44.4% and 60.0% compared to single LiDAR and single infrared methods, respectively. This demonstrates that the proposed method achieves comprehensive optimization in terms of 3D reconstruction accuracy, completeness, and real-time performance under laser demolition rescue conditions. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Optics and photonics Laser Demolition Rescue Scenarios Multi-Source Fusion 3D Reconstruction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 30 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 13 Jan, 2026 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. 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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-8596378","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578637625,"identity":"f3ab34e9-4a86-4af7-8998-3aeaabbea906","order_by":0,"name":"Yi CHEN","email":"","orcid":"","institution":"Hunan Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"CHEN","suffix":""},{"id":578637626,"identity":"9b7d41ae-d7c3-44ae-8589-3fa3d849a464","order_by":1,"name":"Dewen 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