Dynamic Focal Length Adjustment for Drift-Free Cylindrical Panorama Stitching Using Feature-Based Image Alignment

Dynamic Focal Length Adjustment for Drift-Free Cylindrical Panorama Stitching Using Feature-Based Image Alignment | 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 Dynamic Focal Length Adjustment for Drift-Free Cylindrical Panorama Stitching Using Feature-Based Image Alignment Md. Ibrahim Abdullah, Md. Farukuzzaman Khan, Md. Nazrul Islam, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6533055/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 Panoramic image stitching is a fundamental technique in computer vision and graphics , widely utilized in virtual tours, immersive experiences, and robotic vision. However , traditional cylindrical panorama stitching techniques often face challenges with accumulated drift, resulting in visual misalignments in the final output. This paper presents a novel approach to drift-free cylindrical panorama stitching by dynamically adjusting the focal length based on the gap angle between the first and last images. The proposed method incorporates cylindrical warping, robust feature extraction using the Scale-Invariant Feature Transform (SIFT), and image alignment through Random Sample Consensus (RANSAC). Drift correction is achieved by iteratively recalibrating the focal length to minimize alignment errors and ensure seamless stitching across the panoramic sequence. Experimental results demonstrate the effectiveness of the proposed approach in reducing drift, achieving precise alignment, and producing high-quality panoramas. The dynamic focal length adjustment offers significant improvements over existing methods, addressing key challenges in panoramic imaging and enhancing its applicability in real-world scenarios. Cylindrical panorama feature extraction drift correction Face Recognition RANSAC SIFT image stitching 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-6533055","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452554137,"identity":"d298132f-d01f-497f-80fd-d8ebe5d91929","order_by":0,"name":"Md. 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However , traditional cylindrical panorama stitching techniques often face challenges with accumulated drift, resulting in visual misalignments in the final output. This paper presents a novel approach to drift-free cylindrical panorama stitching by dynamically adjusting the focal length based on the gap angle between the first and last images. The proposed method incorporates cylindrical warping, robust feature extraction using the Scale-Invariant Feature Transform (SIFT), and image alignment through Random Sample Consensus (RANSAC). Drift correction is achieved by iteratively recalibrating the focal length to minimize alignment errors and ensure seamless stitching across the panoramic sequence. Experimental results demonstrate the effectiveness of the proposed approach in reducing drift, achieving precise alignment, and producing high-quality panoramas. 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