Scene Reconstruction Based on a Liquid Lens Integrated with a Custom Diffuser

Scene Reconstruction Based on a Liquid Lens Integrated with a Custom Diffuser | 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 Reconstruction Based on a Liquid Lens Integrated with a Custom Diffuser Chang-Jian Siao Yu, Yen-Chang Chu, Sheng-Chun Hung, Jing-Heng Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8686734/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 This study proposes a passive depth-sensing system that integrates a liquid lens with a custom-fabricated diffuser to achieve 2.5D scene reconstruction. The liquid lens leverages the electrowetting effect, enabling swift and precise adjustments of focal length through voltage control, thereby replacing conventional mechanical focusing mechanisms. To enhance depth discrimination, a high-transmittance diffuser featuring randomly structured microfeatures is implemented. This diffuser compresses the system’s depth of field and accentuates variations in image sharpness. Multi-focal images are captured by scanning the liquid lens, and distances to objects are estimated by assessing image sharpness using the Laplacian operator. Experimental results indicate an absolute error of less than ± 1.3 cm and a relative error below 3% within a measurement range of 20 to 70 cm. Moreover, the multi-focal images can be merged to reconstruct 2.5D models containing depth information, which can be exported in the GLB format for cross-platform compatibility. The proposed framework operates independently of the light source, demonstrates robustness under varying environmental conditions, and is computationally efficient, making it ideal for low-cost depth sensing and embedded vision applications. Liquid lens Diffuser Shallow depth of field Depth sensing Image sharpness analysis Scene reconstruction 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. 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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-8686734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580548144,"identity":"32438b84-0b7b-4871-9c4d-7570b2e26c0c","order_by":0,"name":"Chang-Jian Siao Yu","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Chang-Jian","middleName":"Siao","lastName":"Yu","suffix":""},{"id":580548145,"identity":"bf061df0-e19e-44e1-8ed6-76194dc4134c","order_by":1,"name":"Yen-Chang Chu","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Yen-Chang","middleName":"","lastName":"Chu","suffix":""},{"id":580548146,"identity":"f6c7143d-4d65-458e-ba21-9bdbfc07f655","order_by":2,"name":"Sheng-Chun Hung","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Sheng-Chun","middleName":"","lastName":"Hung","suffix":""},{"id":580548147,"identity":"841d1f4e-f48c-4ed4-9de9-a3d0383b90fc","order_by":3,"name":"Jing-Heng Chen","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Jing-Heng","middleName":"","lastName":"Chen","suffix":""},{"id":580548148,"identity":"ad355c34-1205-415e-bbdd-aa437c1ac108","order_by":4,"name":"Kun-Huang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDACCQYGxsYGG34wh4cELWmSDVAtEsRqOUyCFv7ZzcceztxxXoJ/RgLjg7dtDHUGBwhZcudYuuHGM7clJG4kMBvObWOQIKjFQCLHTPJh2+06A4kENmleoBYzwlryvwG1nJMAamH/TaSWHDbJjW0HQFrYmInSInEjzUxyZluyhMSZh82Sc85JSO4npIV/RvIzyd42Own+9uSDH96U2fBDgps4wAhSS0RMjoJRMApGwSggDADB8j0DdhdWLQAAAABJRU5ErkJggg==","orcid":"","institution":"Feng Chia University","correspondingAuthor":true,"prefix":"","firstName":"Kun-Huang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-24 12:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8686734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8686734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101751788,"identity":"d0251dac-4e93-4eb8-8a1c-018b25520b3a","added_by":"auto","created_at":"2026-02-03 10:23:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":899737,"visible":true,"origin":"","legend":"","description":"","filename":"SubmittedpaperliquidlensAppliedPhysicsB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8686734/v1_covered_5472855e-856f-4e54-8ffc-9df45d52db9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scene Reconstruction Based on a Liquid Lens Integrated with a Custom Diffuser","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":"Liquid lens, Diffuser, Shallow depth of field, Depth sensing, Image sharpness analysis, Scene reconstruction","lastPublishedDoi":"10.21203/rs.3.rs-8686734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8686734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a passive depth-sensing system that integrates a liquid lens with a custom-fabricated diffuser to achieve 2.5D scene reconstruction. The liquid lens leverages the electrowetting effect, enabling swift and precise adjustments of focal length through voltage control, thereby replacing conventional mechanical focusing mechanisms. To enhance depth discrimination, a high-transmittance diffuser featuring randomly structured microfeatures is implemented. This diffuser compresses the system\u0026rsquo;s depth of field and accentuates variations in image sharpness. Multi-focal images are captured by scanning the liquid lens, and distances to objects are estimated by assessing image sharpness using the Laplacian operator. Experimental results indicate an absolute error of less than \u0026plusmn;\u0026thinsp;1.3 cm and a relative error below 3% within a measurement range of 20 to 70 cm. Moreover, the multi-focal images can be merged to reconstruct 2.5D models containing depth information, which can be exported in the GLB format for cross-platform compatibility. 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