Robust Point Cloud Normal Estimation via Multi-Level Critical Point Aggregation

Robust Point Cloud Normal Estimation via Multi-Level Critical Point Aggregation | 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 Robust Point Cloud Normal Estimation via Multi-Level Critical Point Aggregation Jun Zhou, Yaoshun Li, Mingjie Wang, Nannan Li, Zhiyang Li, Weixiao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4122754/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In this paper, we propose a multi-level critical point aggregation architecture for 3D point cloud normal estimation. It efficiently focuses on locally important points during feature extraction by employing our Local Feature Aggregation (LFA) and Global Feature Refinement (GFR) modules. These modules can accurately identify critical surface-fitting points across local and global levels. Specifically, the proposed LFA module aims to capture local geometric information from nearby points with strong correlation in low-level features, while our GFR module explores global geometric relationships in high-level features space, focusing on critical global points. Furthermore, utilizing a stacked LFA structure, we address indistinguishable features across multiple levels, enabling deep feature aggregation. By integrating multi-level features through the GFR module, our method effectively integrates robust local geometric information into comprehensive global features. This ensures stability and accuracy in subsequent surface fitting and normal estimation tasks, even in the presence of sharp features, high noise, or anisotropic structures. Experimental results demonstrate that our method is competitive and achieves stable performance on both synthetic and real-world datasets. Our implementation is available at https://github.com/CharlesLee96/NormalEstimation . Point Cloud Processing Normal Estimation Local Feature Aggregation Global Feature Refinement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 May, 2024 Reviews received at journal 11 May, 2024 Reviews received at journal 10 Apr, 2024 Reviewers agreed at journal 24 Mar, 2024 Reviewers agreed at journal 24 Mar, 2024 Reviewers invited by journal 24 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 18 Mar, 2024 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-4122754","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281837746,"identity":"43eec0a8-54b3-43f5-8a61-a4f06da53277","order_by":0,"name":"Jun Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFCCA4wPEgzY5MDMB0RqYTb4UMFnDGYmEGkNm+SMM3KJDSAmUVrkGw+wSfO2maXPDzv8EGiLnZxuAwEtjA0HmK1529JyN95OMwBqSTY2O0BACzPQ+7d5247lbpydANJyIHEbIS1sDAcYgA77n244O/0DcVp4GA4wAb3PliAvnUOkLRIMB5uBgcxmuEE6p+BAggERfpGfcfggKCrl5Wenb/7wocJOjqAWBomDDWDaAKzSgJByEOBvgFrXQIzqUTAKRsEoGJEAAORsR0c3CUWsAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhou","suffix":""},{"id":281837747,"identity":"b40a8579-8579-4ee8-a5d5-4a8ee235d442","order_by":1,"name":"Yaoshun Li","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Yaoshun","middleName":"","lastName":"Li","suffix":""},{"id":281837748,"identity":"77da3846-89fb-4fd3-8beb-04273953f9ca","order_by":2,"name":"Mingjie Wang","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Mingjie","middleName":"","lastName":"Wang","suffix":""},{"id":281837749,"identity":"07f4fa06-469c-4d20-a38d-740fb9735562","order_by":3,"name":"Nannan Li","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Li","suffix":""},{"id":281837750,"identity":"d646ade6-b1e3-4609-b7d5-037e6301a0a0","order_by":4,"name":"Zhiyang Li","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyang","middleName":"","lastName":"Li","suffix":""},{"id":281837751,"identity":"ce57abca-106d-4f52-af46-0e777631d9a2","order_by":5,"name":"Weixiao Wang","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Weixiao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-18 11:21:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4122754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4122754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53224821,"identity":"3ceadf12-4469-45e2-b7cf-7d5e4fd9bb50","added_by":"auto","created_at":"2024-03-22 06:07:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5720232,"visible":true,"origin":"","legend":"","description":"","filename":"tvc2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4122754/v1_covered_b8e94450-31de-4715-a4ee-2c2c28a44046.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Point Cloud Normal Estimation via Multi-Level Critical Point Aggregation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-visual-computer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tvcj","sideBox":"Learn more about [The Visual Computer](http://link.springer.com/journal/371)","snPcode":"371","submissionUrl":"https://submission.nature.com/new-submission/371/3","title":"The Visual Computer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Point Cloud Processing, Normal,Estimation, Local Feature Aggregation, Global Feature Refinement","lastPublishedDoi":"10.21203/rs.3.rs-4122754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4122754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, we propose a multi-level critical point aggregation architecture for 3D point cloud normal estimation. 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