Generating Realistic 3D Surface Defects for Training AI-Based Industrial Inspection Systems

Generating Realistic 3D Surface Defects for Training AI-Based Industrial Inspection Systems | 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 Generating Realistic 3D Surface Defects for Training AI-Based Industrial Inspection Systems Sara Roos Hoefgeest Toribio, Mario Roos-Hoefgeest Toribio, Daniel García Peña, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937326/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Ensuring the surface quality of industrial components requires the detection of small superficial defects—such as cracks, bumps, and peaks—using high-resolution 3D sensors. However, training machine learning algorithms for this task is constrained by the limited availability of annotated 3D defect datasets. In this work, we propose a method for generating synthetic 3D datasets of surface defects using Free-Form Deformation applied to CAD models. The technique allows localized insertion of parametrized defects adapted to the object’s geometry and supports diverse defect types through customizable elevation maps. To simulate realistic sensor acquisition, we replicate the scanning process of a profilometric 3D sensor, including surface and sensor noise. The output consists of labeled depth images that closely resemble real-world sensor data. We validate the approach by training object detection networks on synthetic datasets and evaluating their performance on real samples, demonstrating comparable accuracy. The proposed method reduces reliance on rare and costly real defect data, offering a scalable tool for developing and testing surface inspection systems in industrial contexts. Synthetic defect generation 3D surface inspection Data Augmentation CAD-based modeling Free-Form Deformation Industrial quality control Full Text Supplementary Files video.mp4 Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 29 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers invited by journal 06 Jul, 2025 Editor assigned by journal 24 Jun, 2025 First submitted to journal 20 Jun, 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. 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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-6937326","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481326255,"identity":"db8f394c-5d08-464c-9c35-72a0034e803f","order_by":0,"name":"Sara Roos Hoefgeest 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