Point cloud simplification algorithm based on voxel convex hull

Point cloud simplification algorithm based on voxel convex hull | 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 Point cloud simplification algorithm based on voxel convex hull Haiquan Zhang, Yong Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3862671/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 In order to solve the problem that the speed of feature-based point cloud simplification methods is slow, a point cloud simplification method based on voxel convex hull is proposed in this paper, which avoids the calculation of a large number of features and has good simplification effect. In this method, point cloud is quickly voxelized, and then the voxels are divided into two types according to the number of points in each voxel: less-point voxel and more-point voxel. Less-point voxels are simplified directly using uniform sampling method. More-point voxels are simplified by using the voxel convex hull method. The convex hull volume and directed projection area of points within each more-point voxel are calculated as voxels’ feature values, and then the point cloud are divided into groups according to the feature values and points in different groups are simplified separately. Finally, the simplified point cloud is obtained by fusing the simplification results of less-point voxels and more-point voxels. The reduction ratio of point cloud is controllable and the parameters are easy to be set in this method. Experiments show that the simplification time of this method is more than 50% lower than that of the simplification method based on curvature and clustering. The retention of point cloud features of this method is obviously higher than that of traditional methods such as uniform sampling method, and the average curvature is improved by about 10%. Moreover, the reduction ratio of this method is controllable, and the parameters are flexible and easy to be set. point cloud simplification convex hull voxelization average curvature 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-3862671","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267403493,"identity":"ba4cd726-111d-47a2-89c5-6a72c877822a","order_by":0,"name":"Haiquan Zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Haiquan","middleName":"","lastName":"Zhang","suffix":""},{"id":267403494,"identity":"51b9277c-008d-48a8-804b-3899e582d82b","order_by":1,"name":"Yong Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPigtB6HYiNACU2NMupbEBuK1SCQfk/i4ozZ9fv8ZA4YPZYcZ+Gc3ENKSliY588zx3MaGMwaMM84dZpC4c4CQlhwzad62Y7nNjD0GzLxthxkMJBKI05LOxsxjwPyXBC01CTxsQC2MRGnheZZsObPtgOEMHraCgz3n0nkkbhDQws+efPDGx7Y6efn+wxsf/CizluOfQUALELBIMDAcBrMOADEPQfVAwPyBgaGOGIWjYBSMglEwUgEAUGg6LaOnaMsAAAAASUVORK5CYII=","orcid":"","institution":"Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-01-14 08:29:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3862671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3862671/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51674815,"identity":"1c7eca14-6a2a-41eb-b23e-5deb1fa821c1","added_by":"auto","created_at":"2024-02-27 04:25:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265981,"visible":true,"origin":"","legend":"","description":"","filename":"Simplificationbasedonvoxelconvexhull.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3862671/v1_covered_05c748b0-9c44-4943-b360-e4f48a50f124.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Point cloud simplification algorithm based on voxel convex hull","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"point cloud, simplification, convex hull, voxelization, average curvature","lastPublishedDoi":"10.21203/rs.3.rs-3862671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3862671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to solve the problem that the speed of feature-based point cloud simplification methods is slow, a point cloud simplification method based on voxel convex hull is proposed in this paper, which avoids the calculation of a large number of features and has good simplification effect. In this method, point cloud is quickly voxelized, and then the voxels are divided into two types according to the number of points in each voxel: less-point voxel and more-point voxel. Less-point voxels are simplified directly using uniform sampling method. More-point voxels are simplified by using the voxel convex hull method. The convex hull volume and directed projection area of points within each more-point voxel are calculated as voxels\u0026rsquo; feature values, and then the point cloud are divided into groups according to the feature values and points in different groups are simplified separately. Finally, the simplified point cloud is obtained by fusing the simplification results of less-point voxels and more-point voxels. The reduction ratio of point cloud is controllable and the parameters are easy to be set in this method. 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