3D Model Reconstruction and Texture Detail Restoration Technology of Digital Sculptures Using Masked Autoencoder

3D Model Reconstruction and Texture Detail Restoration Technology of Digital Sculptures Using Masked Autoencoder | 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 3D Model Reconstruction and Texture Detail Restoration Technology of Digital Sculptures Using Masked Autoencoder Yu Chen, Hong Lyu, Shazhi Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7433006/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract To protect and share cultural heritage, it is important to accurately recreate three-dimensional (3D) digital sculptures. But photogrammetry- and CNN-based approaches that are already out there typically have problems with occlusions, partial scans, and reflecting surfaces, which lead to geometric errors and texture loss. To tackle these issues, we provide a single Masked Autoencoder (MAE)-based architecture that can rebuild geometry and restore high-fidelity textures at the same time. The architecture combines RGB, depth, and fluorescence scans with a hybrid loss function that balances Chamfer Distance, perceptual similarity, and structural consistency. Fluorescence-guided texture enhancement makes it much easier to find small pigment differences and microstructural features that are typically missed in regular RGB imaging. We confirmed the suggested strategy using a freshly created Digital Sculpture Dataset (DSD) that includes 120 high-resolution cultural objects. Quantitative assessments exhibited enhanced performance compared to leading-edge benchmarks, including PointNet++, AtlasNet, and NeRF versions, attaining reduced Chamfer Distance, elevated F-scores, and superior perceptual metrics (PSNR, SSIM, and LPIPS). Qualitative evaluations validated the precise reconstruction of both macroscopic geometry and intricate surface textures, even under scenarios of occlusion or deterioration. The suggested framework is a strong and flexible way to preserve digital information with high accuracy. It may be used in virtual museums, conservation monitoring, and heritage research. This study improves digital archiving by combining geometry reconstruction and texture restoration into a single end-to-end process. It also opens up new possibilities for historically accurate 3D modeling of cultural relics. 3D Reconstruction masked autoencoder (MAE) texture restoration cultural heritage preservation fluorescence imaging digital sculpture dataset (DSD) Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 23 Sep, 2025 Editor invited by journal 25 Aug, 2025 First submitted to journal 22 Aug, 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-7433006","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519617643,"identity":"145deddf-edad-4db8-90d1-42b377f8eea7","order_by":0,"name":"Yu 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