Applications of Deep Learning in ArchitecturalFacade Parsing and 3D Reconstruction

Applications of Deep Learning in ArchitecturalFacade Parsing and 3D Reconstruction | 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 Applications of Deep Learning in ArchitecturalFacade Parsing and 3D Reconstruction Shihao Guo, Yachun Hu, Jing Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7110965/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The rapid advancements in deep learning have significantly transformed architectural facade parsing and 3D reconstruction,enabling precise and scalable analysis of urban environments. Traditional approaches, such as rule-based methods andhandcrafted feature extraction, often struggle with variations in architectural styles, occlusions, and perspective distortions.While convolutional neural networks (CNNs) have demonstrated considerable success in facade segmentation, they often lackstructural awareness, leading to inconsistent and fragmented parsing results. To address these challenges, we propose a novelHierarchical Structural Parsing Network (HSPN) that integrates multi-scale feature extraction, graph-based structural reasoning,and semantic refinement modules. Our method effectively captures both global architectural layout and fine-grained detailsby incorporating geometric constraints and adaptive structural consistency optimization (ASCO). Extensive experiments onbenchmark datasets demonstrate that our approach outperforms state-of-the-art methods in segmentation accuracy andarchitectural coherence. The proposed framework provides a robust solution for facade parsing and has potential applicationsin automated city modeling, digital twin construction, and immersive virtual environments. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Facade Parsing 3D Reconstruction Deep Learning CNN Transformer Feature Fusion Geometric Constraints Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 17 Jul, 2025 Editor invited by journal 17 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 12 Jul, 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. 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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-7110965","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487950237,"identity":"3e836e4c-16a0-4279-be97-133b802108a2","order_by":0,"name":"Shihao Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACZuYGCOMAYwPDRzCbsfEAfi1AlQwJEC2MMxsYJIBaGvBrYYBrAWrnBWsBs3EDc3bGNomPP+7J8d0+3CZtu8OmTrf9MNCWGptoXFosmxnbJGckFBtLnktsNs49kyZhdiYRqOVYWm4DDi0GhxmbjXkSEhI3nGFsfJzbdljC7ABQC2PDYfxa/kC0NBy2BGk5/5CglsbHDDBbGEFabhCwBeiXxoc9aQnGkmcYmw1729Ikt90A2pKAxy/m/IcPHPhhkyDHd4b9mcTPNht+s/PpDx98qLHB7TDswgk4lOPRMgpGwSgYBaMACQAAKM9kz7/UF50AAAAASUVORK5CYII=","orcid":"","institution":"City University of Zhengzhou","correspondingAuthor":true,"prefix":"","firstName":"Shihao","middleName":"","lastName":"Guo","suffix":""},{"id":487950238,"identity":"8d78c317-7d56-4d32-9dc4-e4d7b4a4fb3c","order_by":1,"name":"Yachun Hu","email":"","orcid":"","institution":"City University of Zhengzhou","correspondingAuthor":false,"prefix":"","firstName":"Yachun","middleName":"","lastName":"Hu","suffix":""},{"id":487950239,"identity":"3b39f3ca-87d8-4d5d-a7a5-4b348bcca6fb","order_by":2,"name":"Jing Xu","email":"","orcid":"","institution":"City University of Zhengzhou","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-07-13 03:53:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7110965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7110965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87262694,"identity":"b0d41611-416c-4fe6-a3b0-95aad106ed05","added_by":"auto","created_at":"2025-07-22 07:29:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1455609,"visible":true,"origin":"","legend":"","description":"","filename":"SR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7110965/v1_covered_c86ada35-0f29-4ad4-8cd3-f162901da87a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applications of Deep Learning in ArchitecturalFacade Parsing and 3D Reconstruction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Facade Parsing, 3D Reconstruction, Deep Learning, CNN, Transformer, Feature Fusion, Geometric Constraints","lastPublishedDoi":"10.21203/rs.3.rs-7110965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7110965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid advancements in deep learning have significantly transformed architectural facade parsing and 3D reconstruction,enabling precise and scalable analysis of urban environments. Traditional approaches, such as rule-based methods andhandcrafted feature extraction, often struggle with variations in architectural styles, occlusions, and perspective distortions.While convolutional neural networks (CNNs) have demonstrated considerable success in facade segmentation, they often lackstructural awareness, leading to inconsistent and fragmented parsing results. To address these challenges, we propose a novelHierarchical Structural Parsing Network (HSPN) that integrates multi-scale feature extraction, graph-based structural reasoning,and semantic refinement modules. Our method effectively captures both global architectural layout and fine-grained detailsby incorporating geometric constraints and adaptive structural consistency optimization (ASCO). Extensive experiments onbenchmark datasets demonstrate that our approach outperforms state-of-the-art methods in segmentation accuracy andarchitectural coherence. 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