A Novel Point Cloud Simplification Method Based on Clustering and Saliency for 3D reconstruction of cultural relics

A Novel Point Cloud Simplification Method Based on Clustering and Saliency for 3D reconstruction of cultural relics | 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 Article A Novel Point Cloud Simplification Method Based on Clustering and Saliency for 3D reconstruction of cultural relics Jian Li, Chenyang Peng, Wanfa Gu, Hao Cui, Xiaoqian Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252831/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Sep, 2025 Read the published version in npj Heritage Science → Version 1 posted 10 You are reading this latest preprint version Abstract With the rapid development of 3D scanning technologies, high-density point clouds of cultural heritage artifacts such as stone carvings, statues pose significant challenges in storage, processing, and accurate reconstruction. This paper proposes a point cloud simplification method tailored for cultural heritage applications, combining clustering and saliency analysis to preserve intricate surface details critical for archaeological studies. By segmenting point clouds into clusters with normal vector constraints and evaluating saliency through roughness and curvature metrics, our method adaptively retains primary features including engraved patterns weathered textures while simplifying non-feature regions. Experiments on stone carvings from the Northern Song Imperial Mausoleum, Terracotta Warriors, and Stanford datas demonstrate that the algorithm effectively avoids mesh holes and maintains geometric fidelity, enabling efficient 3D reconstruction for heritage conservation. This work bridges advanced point cloud processing with practical archaeological needs, offering a robust tool for digitizing and analyzing cultural relics with minimal loss of historically significant details. archaeological studies point cloud simplification clustering roughness curvature saliency Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 09 Sep, 2025 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 25 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers invited by journal 30 Mar, 2025 Editor assigned by journal 25 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 18 Mar, 2025 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. 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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-6252831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444223624,"identity":"d45dff07-ac18-42ba-9a40-db2d320fd80b","order_by":0,"name":"Jian Li","email":"","orcid":"","institution":"Zhengzhou university","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Li","suffix":""},{"id":444223625,"identity":"1e1a3152-1d2c-479b-a435-d66b144a2285","order_by":1,"name":"Chenyang Peng","email":"","orcid":"","institution":"Henan Thinker Automatic Equipment Co., 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