Towards AI-Powered Automatic 3D Scene Generation for the Metaverse: A Comparative Analysis of Manual and Photogrammetry Techniques

Towards AI-Powered Automatic 3D Scene Generation for the Metaverse: A Comparative Analysis of Manual and Photogrammetry Techniques | 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 Towards AI-Powered Automatic 3D Scene Generation for the Metaverse: A Comparative Analysis of Manual and Photogrammetry Techniques Viviana Pentangelo, Dario Di Dario, Vincenzo De Martino, Marco Dello Buono, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6558211/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract The development of the metaverse relies on the creation of realistic, immersive, and high-performance 3D scenes. For such a reason, manual construction of these scenes remains a time-consuming process that requires advanced technical skills, posing a significant barrier to widespread adoption. Recent advancements in AI-assisted photogrammetry tools offer an alternative approach by enabling semi-automatic 3D reconstruction from real-world environments. Nevertheless, the usability, quality, and real-time applicability of the resulting assets remain underexplored.This study aims to assess the effectiveness of photogrammetry-based tools for creating functional and visually appealing 3D scenes for metaverse applications.To this end, a comparative experiment was conducted by reconstructing three environments using manual modeling and two photogrammetry tools based on distinct technologies: Polycam (MVS-based) and LumaAI (NeRF-based). The resulting models were evaluated using quantitative metrics, including modeling time, polygon count, visual similarity (SSIM), real-time performance (GPU usage and FPS), and Hausdorff distance.The findings revealed that photogrammetry significantly accelerates the modeling process but does not consistently outperform manual modeling in terms of polygonal optimization and real-time rendering efficiency. Furthermore, the choice between photogrammetry tools depends on object characteristics and application constraints.This study provides practical insights and empirical guidelines for developers and researchers, highlighting the trade-offs between automation, performance, and visual fidelity in 3D scene generation for the metaverse. Metaverse Photogrammetry 3D Scene Generation Virtual Environments Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 02 May, 2025 Editor assigned by journal 30 Apr, 2025 Submission checks completed at journal 30 Apr, 2025 First submitted to journal 29 Apr, 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-6558211","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452182835,"identity":"9034ea30-df40-44cb-b48e-db4c3e7f9f2e","order_by":0,"name":"Viviana Pentangelo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFADdgbGBwwMEkAWYwNh1QdABDMDswFUSyNhPVAtbBJQPn5rDG43H3v8oeaOXT8z87Fq3h0W9vwNzO0P8Gq5cyzd4MCxZ8kzm9nSbvOekUiccYCAwyRn5JhJHGA7nGxwmMfsNm+bRAIDYS353yQO/DucbA/UUgzUYi9PSAu/RA6bxMG2w3YGzDxmzEAtjBsIapE5ZiZxtu9wgsRhtmTJuW0SiRsPMzbOwKeFTbr5mUTFt8P2/O3NBz+8bauzlzve/uADPi0M0LhIRLiFGa96hBZ7QupGwSgYBaNgBAMAxWVKOTDfjKIAAAAASUVORK5CYII=","orcid":"","institution":"University of Salerno","correspondingAuthor":true,"prefix":"","firstName":"Viviana","middleName":"","lastName":"Pentangelo","suffix":""},{"id":452182836,"identity":"eceb9f0c-4f9f-4a4f-bd2e-8fccdb85389c","order_by":1,"name":"Dario Di Dario","email":"","orcid":"","institution":"University of Salerno","correspondingAuthor":false,"prefix":"","firstName":"Dario","middleName":"Di","lastName":"Dario","suffix":""},{"id":452182838,"identity":"d049492d-6489-4627-9c2d-52af1c26cb30","order_by":2,"name":"Vincenzo De Martino","email":"","orcid":"","institution":"University of Salerno","correspondingAuthor":false,"prefix":"","firstName":"Vincenzo","middleName":"","lastName":"De Martino","suffix":""},{"id":452182839,"identity":"4800bf75-16f0-42b6-a52b-a54f9d31a8a4","order_by":3,"name":"Marco Dello Buono","email":"","orcid":"","institution":"University of Salerno","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Dello","lastName":"Buono","suffix":""},{"id":452182840,"identity":"5fc2231e-f091-426e-86ef-54e8fff6224e","order_by":4,"name":"Stefano Lambiase","email":"","orcid":"","institution":"University of Salerno","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Lambiase","suffix":""}],"badges":[],"createdAt":"2025-04-29 16:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6558211/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6558211/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82144895,"identity":"50e82cfd-100f-4b0d-aca7-85a737a0f22f","added_by":"auto","created_at":"2025-05-07 06:48:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1745386,"visible":true,"origin":"","legend":"","description":"","filename":"ClusterComputingiMeta2024Extension.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558211/v1_covered_1630ca1c-985e-4281-a096-9aa011625ef0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards AI-Powered Automatic 3D Scene Generation for the Metaverse: A Comparative Analysis of Manual and Photogrammetry Techniques","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Metaverse, Photogrammetry, 3D Scene Generation, Virtual Environments","lastPublishedDoi":"10.21203/rs.3.rs-6558211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6558211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The development of the metaverse relies on the creation of realistic, immersive, and high-performance 3D scenes. 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