Non-Photorealistic Rendering for 3D Digitalization of Chinese Painting: Technique–Algorithm Matrix and Case Study of Qian Xuan’s Eight Flowers

Non-Photorealistic Rendering for 3D Digitalization of Chinese Painting: Technique–Algorithm Matrix and Case Study of Qian Xuan’s Eight Flowers | 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 Non-Photorealistic Rendering for 3D Digitalization of Chinese Painting: Technique–Algorithm Matrix and Case Study of Qian Xuan’s Eight Flowers Meiru Yin, Wenzhuo Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7651404/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 Traditional Chinese painting is an essential component of cultural heritage, and its 3D digitalization plays a crucial role in cultural dissemination and artistic reproduction. However, existing Non-Photorealistic Rendering (NPR) approaches face limitations in reproducing Chinese paintings, such as loss of artistic spirit, unclear workflows, and high learning costs for creators. To address these issues, this study introduces a “Chinese Painting Technique–NPR Algorithm Matching Matrix,” which quantitatively maps five major categories of traditional techniques to 24 mainstream NPR algorithms, providing systematic guidelines for algorithm selection. Based on this framework, we develop a layered collaborative 3D digitalization pipeline, exemplified through Qian Xuan’s Eight Flowers , and incorporate an Artist-in-the-loop mechanism to ensure artistic fidelity. Furthermore, a multidimensional evaluation system was established, combining general metrics (LPIPS, SSIM) with newly designed indicators for color fidelity, brushstroke texture, surface texture, and glossiness, alongside subjective evaluation via blind tests. The results demonstrate that our method achieves a favorable balance between artistic authenticity and computational efficiency, outperforming existing projects in both objective scores and perceptual realism. This research not only provides a new pathway for the digital preservation and dissemination of Chinese paintings but also extends the application potential of NPR techniques in cultural heritage. Humanities/Cultural and media studies Social science/Cultural and media studies Physical sciences/Mathematics and computing Chinese painting digitalization Non-photorealistic rendering Technique–algorithm matrix Multidimensional evaluation Cultural heritage Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 11 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor assigned by journal 22 Oct, 2025 Editor invited by journal 16 Oct, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 26 Sep, 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. 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-7651404","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538386125,"identity":"60233d40-343e-4e62-88b2-31bbdc20456e","order_by":0,"name":"Meiru 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Xuan’s\u003cem\u003e Eight Flowers\u003c/em\u003e\u003c/p\u003e","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":"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 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However, existing Non-Photorealistic Rendering (NPR) approaches face limitations in reproducing Chinese paintings, such as loss of artistic spirit, unclear workflows, and high learning costs for creators. To address these issues, this study introduces a \u0026ldquo;Chinese Painting Technique\u0026ndash;NPR Algorithm Matching Matrix,\u0026rdquo; which quantitatively maps five major categories of traditional techniques to 24 mainstream NPR algorithms, providing systematic guidelines for algorithm selection. Based on this framework, we develop a layered collaborative 3D digitalization pipeline, exemplified through Qian Xuan\u0026rsquo;s \u003cem\u003eEight Flowers\u003c/em\u003e, and incorporate an Artist-in-the-loop mechanism to ensure artistic fidelity. Furthermore, a multidimensional evaluation system was established, combining general metrics (LPIPS, SSIM) with newly designed indicators for color fidelity, brushstroke texture, surface texture, and glossiness, alongside subjective evaluation via blind tests. The results demonstrate that our method achieves a favorable balance between artistic authenticity and computational efficiency, outperforming existing projects in both objective scores and perceptual realism. This research not only provides a new pathway for the digital preservation and dissemination of Chinese paintings but also extends the application potential of NPR techniques in cultural heritage.\u003c/p\u003e","manuscriptTitle":"Non-Photorealistic Rendering for 3D Digitalization of Chinese Painting: Technique–Algorithm Matrix and Case Study of Qian Xuan’s Eight Flowers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 10:11:30","doi":"10.21203/rs.3.rs-7651404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T08:16:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T07:24:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97204684726989271549830176662240457139","date":"2026-03-10T07:41:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T11:02:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T07:18:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117710500222959431522093365211018154083","date":"2025-10-28T09:51:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124675914288407770983523822561253374574","date":"2025-10-22T10:00:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T08:41:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-22T08:40:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-16T10:36:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T13:21:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-26T13:16:35+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"b191d9f5-a207-4044-8e55-f5694510af72","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57328906,"name":"Humanities/Cultural and media studies"},{"id":57328907,"name":"Social science/Cultural and media studies"},{"id":57328908,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-28T07:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 10:11:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7651404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7651404","identity":"rs-7651404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}