A subdivision-based framework for shape reconstruction

Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Multimedia tools and applications 2024-07, Vol.83 (25), p.65773-65788
Hauptverfasser: Shaolong, Liu, Na, Liu, Chenlei, Lv, Dan, Zhang
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 65788
container_issue 25
container_start_page 65773
container_title Multimedia tools and applications
container_volume 83
creator Shaolong, Liu
Na, Liu
Chenlei, Lv
Dan, Zhang
description Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: ( https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction )
doi_str_mv 10.1007/s11042-023-15398-7
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3077578169</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3077578169</sourcerecordid><originalsourceid>FETCH-LOGICAL-c270t-c489477efff8117e7798a7106049f7d77c5595856c8f81f5b2d03e990ddd60c53</originalsourceid><addsrcrecordid>eNp9kEtLxDAUhYMoOI7-AVcF19GbpOlNlsPgCwbc6Dp08tCOTjMmreK_N1pBV67uWXznXPgIOWVwzgDwIjMGNafABWVSaEVxj8yYREEROdv_kw_JUc4bANZIXs9Is6jyuHbdW5e72NN1m72rQmq3_j2m5yrEVOWnduer5G3s85BGOxTwmByE9iX7k587Jw9Xl_fLG7q6u75dLlbUcoSB2lrpGtGHEBRj6BG1apFBA7UO6BCtlFoq2VhVgCDX3IHwWoNzrgErxZycTbu7FF9HnweziWPqy0sjAFGiYo0uFJ8om2LOyQezS922TR-GgfnyYyY_pvgx334MlpKYSrnA_aNPv9P_tD4B7zNnQA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3077578169</pqid></control><display><type>article</type><title>A subdivision-based framework for shape reconstruction</title><source>Springer Nature - Complete Springer Journals</source><creator>Shaolong, Liu ; Na, Liu ; Chenlei, Lv ; Dan, Zhang</creator><creatorcontrib>Shaolong, Liu ; Na, Liu ; Chenlei, Lv ; Dan, Zhang</creatorcontrib><description>Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: ( https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction )</description><identifier>ISSN: 1573-7721</identifier><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-023-15398-7</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Approximation ; Computer Communication Networks ; Computer graphics ; Computer Science ; Controllability ; Data Structures and Information Theory ; Image reconstruction ; Mesh generation ; Methods ; Metric space ; Multimedia ; Multimedia Information Systems ; Optimization ; Shape optimization ; Special Purpose and Application-Based Systems ; Three dimensional models</subject><ispartof>Multimedia tools and applications, 2024-07, Vol.83 (25), p.65773-65788</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-c489477efff8117e7798a7106049f7d77c5595856c8f81f5b2d03e990ddd60c53</cites><orcidid>0000-0002-8203-3118</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-023-15398-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-023-15398-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Shaolong, Liu</creatorcontrib><creatorcontrib>Na, Liu</creatorcontrib><creatorcontrib>Chenlei, Lv</creatorcontrib><creatorcontrib>Dan, Zhang</creatorcontrib><title>A subdivision-based framework for shape reconstruction</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: ( https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction )</description><subject>Accuracy</subject><subject>Approximation</subject><subject>Computer Communication Networks</subject><subject>Computer graphics</subject><subject>Computer Science</subject><subject>Controllability</subject><subject>Data Structures and Information Theory</subject><subject>Image reconstruction</subject><subject>Mesh generation</subject><subject>Methods</subject><subject>Metric space</subject><subject>Multimedia</subject><subject>Multimedia Information Systems</subject><subject>Optimization</subject><subject>Shape optimization</subject><subject>Special Purpose and Application-Based Systems</subject><subject>Three dimensional models</subject><issn>1573-7721</issn><issn>1380-7501</issn><issn>1573-7721</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLxDAUhYMoOI7-AVcF19GbpOlNlsPgCwbc6Dp08tCOTjMmreK_N1pBV67uWXznXPgIOWVwzgDwIjMGNafABWVSaEVxj8yYREEROdv_kw_JUc4bANZIXs9Is6jyuHbdW5e72NN1m72rQmq3_j2m5yrEVOWnduer5G3s85BGOxTwmByE9iX7k587Jw9Xl_fLG7q6u75dLlbUcoSB2lrpGtGHEBRj6BG1apFBA7UO6BCtlFoq2VhVgCDX3IHwWoNzrgErxZycTbu7FF9HnweziWPqy0sjAFGiYo0uFJ8om2LOyQezS922TR-GgfnyYyY_pvgx334MlpKYSrnA_aNPv9P_tD4B7zNnQA</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Shaolong, Liu</creator><creator>Na, Liu</creator><creator>Chenlei, Lv</creator><creator>Dan, Zhang</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-8203-3118</orcidid></search><sort><creationdate>20240701</creationdate><title>A subdivision-based framework for shape reconstruction</title><author>Shaolong, Liu ; Na, Liu ; Chenlei, Lv ; Dan, Zhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-c489477efff8117e7798a7106049f7d77c5595856c8f81f5b2d03e990ddd60c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Approximation</topic><topic>Computer Communication Networks</topic><topic>Computer graphics</topic><topic>Computer Science</topic><topic>Controllability</topic><topic>Data Structures and Information Theory</topic><topic>Image reconstruction</topic><topic>Mesh generation</topic><topic>Methods</topic><topic>Metric space</topic><topic>Multimedia</topic><topic>Multimedia Information Systems</topic><topic>Optimization</topic><topic>Shape optimization</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Three dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shaolong, Liu</creatorcontrib><creatorcontrib>Na, Liu</creatorcontrib><creatorcontrib>Chenlei, Lv</creatorcontrib><creatorcontrib>Dan, Zhang</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shaolong, Liu</au><au>Na, Liu</au><au>Chenlei, Lv</au><au>Dan, Zhang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A subdivision-based framework for shape reconstruction</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2024-07-01</date><risdate>2024</risdate><volume>83</volume><issue>25</issue><spage>65773</spage><epage>65788</epage><pages>65773-65788</pages><issn>1573-7721</issn><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: ( https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction )</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-023-15398-7</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-8203-3118</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 1573-7721
ispartof Multimedia tools and applications, 2024-07, Vol.83 (25), p.65773-65788
issn 1573-7721
1380-7501
1573-7721
language eng
recordid cdi_proquest_journals_3077578169
source Springer Nature - Complete Springer Journals
subjects Accuracy
Approximation
Computer Communication Networks
Computer graphics
Computer Science
Controllability
Data Structures and Information Theory
Image reconstruction
Mesh generation
Methods
Metric space
Multimedia
Multimedia Information Systems
Optimization
Shape optimization
Special Purpose and Application-Based Systems
Three dimensional models
title A subdivision-based framework for shape reconstruction
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T09%3A10%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20subdivision-based%20framework%20for%20shape%20reconstruction&rft.jtitle=Multimedia%20tools%20and%20applications&rft.au=Shaolong,%20Liu&rft.date=2024-07-01&rft.volume=83&rft.issue=25&rft.spage=65773&rft.epage=65788&rft.pages=65773-65788&rft.issn=1573-7721&rft.eissn=1573-7721&rft_id=info:doi/10.1007/s11042-023-15398-7&rft_dat=%3Cproquest_cross%3E3077578169%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3077578169&rft_id=info:pmid/&rfr_iscdi=true