Advancing Industrial Vision Research with SyGRID: Synthetically Generated Realistic Industrial Dataset

Advancing Industrial Vision Research with SyGRID: Synthetically Generated Realistic Industrial Dataset | 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 Advancing Industrial Vision Research with SyGRID: Synthetically Generated Realistic Industrial Dataset Elena Govi, Davide Sapienza, Luca De Dominicis, Nicola Capodieci, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8017422/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 Industrial automation depends on accurate object recognition and localization tasks, such as depth estimation, instance segmentation, object detection, and 6D pose estimation. Despite significant advancements, numerous challenges persist, especially within industrial settings. To address these challenges, we propose SyGRID, (Synthetically Generated Realistic Industrial Dataset), a new simulated, realistic dataset specifically designed for industrial use cases. \textcolor{black}{In addition, we propose a robotic vision pipeline based on instance segmentation, 6D pose estimation, and vision-based picking strategies with real-world experiments to prove the effectiveness of the dataset in industrial applications.}Its novelty lies in several aspects: the generated frames are photo-realistic images of objects commonly used in industrial settings, capturing their unique material properties; this includes reflection and refraction under varying environmental light conditions. Moreover, SyGRID includes multi-object and multi-instance cluttered scenes accurately accounting for rigid-body physics. Aiming to narrow the currently existing gap between research and industrial applications, we also provide an exhaustive study on different tasks: namely 2D detection, segmentation, and 6D pose estimation. These tasks of computer vision are essential for the integration of robotic applications such as grasping.SyGRID can significantly contribute to industrial tasks, leading to more reliable robotic operations. By providing this dataset, we aim to accelerate advancements in robotic automation, facilitating the alignment of current progress in computer vision with the practical demands of industrial robotic applications. Data Sets for Robotic Vision Deep Learning for Visual Perception Computer Vision for Automation 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-8017422","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578007767,"identity":"012e6f5c-58eb-4a11-8d41-68846a68d856","order_by":0,"name":"Elena 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