Scene-adaptive RTI acquisition guided by dataset quality analysis

Scene-adaptive RTI acquisition guided by dataset quality analysis | 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 Scene-adaptive RTI acquisition guided by dataset quality analysis Muhammad Arsalan Khawaja, Sony George, Franck Marzani, Jon Yngve Hardeberg, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9360084/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 Reflectance Transformation Imaging (RTI) is widely used for the documentation and visual analysis of cultural heritage artefacts. However, RTI acquisitions are typically performed using fixed or heuristic light configurations that do not account for the reflectance and geometric properties of the observed scene. As a result, datasets may contain redundant sampling in some regions of the light hemisphere while remaining insufficient in others.This paper proposes a framework for analysing the quality of RTI datasets in order to guide scene-adaptive acquisition strategies. The approach starts from a sparse pilot acquisition. We interpret RTI acquisitions as samples of the reflectance response of surfaces across illumination space. By analysing the structure of these reflectance responses, the proposed framework identifies sampling imbalances and suggests adaptive illumination directions.The framework performs two complementary analyses. First, a global analysis is conducted in the light-position space, where the Sampling Balance Indicator (SBI) quantifies the relationship between angular distances of neighbouring light directions and their corresponding image differences in order to detect sampling imbalance. Second, a local analysis is performed in the image domain, where pixel-wise reflectance responses are analysed using Self-Organizing Maps (SOMs) to identify heterogeneous surface behaviours across the scene.Based on these analyses, the framework proposes global and local light-position suggestion strategies aimed at improving illumination sampling and enabling scene-adaptive RTI acquisition. Experiments on both synthetic and real multi-light image collections show that the proposed analysis reveals significant variations in sampling behaviour across different surfaces and provides practical guidance for designing improved RTI acquisitions. Reflectance Transformation Imaging illumination sampling dataset quality analysis scene-adaptive acquisition cultural heritage imaging Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9360084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621557935,"identity":"6e79fdc5-930a-4035-97e8-829d7efb0159","order_by":0,"name":"Muhammad Arsalan Khawaja","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYFACHjDJ2MDAwMwMYTEfIFkLWwIDfk3oWoAiBni16M7IPfjpZo6dbAP7AWbjgprD8sztZ749/rjjMIM5ewNWLWY38pKlc7clGzfwJDAnzzh22LCxJ3e7wcEzhxkse7DbZXYjxwCohTmxgSGB+TAP223GxobcbRIH2w4zGNxIwKXF+HfutvrEBv4HQC3/bts39r95RkiLGdCWw4kNEkCH8bbdTmyckcOGX8uZN2bWuduOG7dJPGw25u37n9w445mZxNm2dB6DMzj8cjzH+HbutmrZfv7kw9I839JsN/YnP5OobLOWMziOPcTggA0cNUBgCFXIg189MpAnXukoGAWjYBSMEAAABvVlr7S4ZuIAAAAASUVORK5CYII=","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Arsalan","lastName":"Khawaja","suffix":""},{"id":621557937,"identity":"8f24969f-e603-4901-8981-5cc4add40623","order_by":1,"name":"Sony George","email":"","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":false,"prefix":"","firstName":"Sony","middleName":"","lastName":"George","suffix":""},{"id":621557938,"identity":"6913445d-6832-4d4f-a613-31bf44f3b845","order_by":2,"name":"Franck Marzani","email":"","orcid":"","institution":"Université Bourgogne Europe","correspondingAuthor":false,"prefix":"","firstName":"Franck","middleName":"","lastName":"Marzani","suffix":""},{"id":621557940,"identity":"96b9a4fd-57f8-4d25-8f3f-babe9d5d2b1c","order_by":3,"name":"Jon Yngve Hardeberg","email":"","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"Yngve","lastName":"Hardeberg","suffix":""},{"id":621557941,"identity":"29b1c2ed-01d9-49a0-825d-36b171c5d7cf","order_by":4,"name":"Alamin Mansouri","email":"","orcid":"","institution":"Université Bourgogne Europe","correspondingAuthor":false,"prefix":"","firstName":"Alamin","middleName":"","lastName":"Mansouri","suffix":""}],"badges":[],"createdAt":"2026-04-08 17:54:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9360084/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9360084/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108491912,"identity":"52afb977-5176-4740-b6c7-06d70a92f4d2","added_by":"auto","created_at":"2026-05-05 09:56:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":77422921,"visible":true,"origin":"","legend":"","description":"","filename":"Journalv1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9360084/v1_covered_7249a6b1-4d38-47e2-a4b2-d8d373e3de44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scene-adaptive RTI acquisition guided by dataset quality analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Reflectance Transformation Imaging, illumination sampling, dataset quality analysis, scene-adaptive acquisition, cultural heritage imaging","lastPublishedDoi":"10.21203/rs.3.rs-9360084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9360084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReflectance Transformation Imaging (RTI) is widely used for the documentation and visual analysis of cultural heritage artefacts. However, RTI acquisitions are typically performed using fixed or heuristic light configurations that do not account for the reflectance and geometric properties of the observed scene. As a result, datasets may contain redundant sampling in some regions of the light hemisphere while remaining insufficient in others.This paper proposes a framework for analysing the quality of RTI datasets in order to guide scene-adaptive acquisition strategies. The approach starts from a sparse pilot acquisition. We interpret RTI acquisitions as samples of the reflectance response of surfaces across illumination space. By analysing the structure of these reflectance responses, the proposed framework identifies sampling imbalances and suggests adaptive illumination directions.The framework performs two complementary analyses. 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