A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing
The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for onl...
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| Veröffentlicht in: | Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-16 |
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| container_title | Mathematical problems in engineering |
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| creator | Wang, Hui Zhang, Chenglei Yin, Xiyan Zhao, Feiyu Sheng, Buyun Huang, Peide |
| description | The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment. |
| doi_str_mv | 10.1155/2018/4673849 |
| format | Article |
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Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2018/4673849</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>3-D printers ; Algorithms ; Cloud computing ; Computer graphics ; Data transmission ; Engineering ; Least squares method ; Lightweight ; Mathematical problems ; On-line systems ; Pretreatment ; Product development ; Rapid prototyping ; Reconstruction ; Registration ; Scanners ; Scanning ; Three dimensional models ; Three dimensional printing ; Visualization ; Weight reduction</subject><ispartof>Mathematical problems in engineering, 2018-01, Vol.2018 (2018), p.1-16</ispartof><rights>Copyright © 2018 Buyun Sheng et al.</rights><rights>Copyright © 2018 Buyun Sheng et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-49836c7893cde045f614db82e4cdbdf7616c6a7ca84a47909afb3c66db4d71473</citedby><cites>FETCH-LOGICAL-c360t-49836c7893cde045f614db82e4cdbdf7616c6a7ca84a47909afb3c66db4d71473</cites><orcidid>0000-0002-5747-7614 ; 0000-0001-7469-8358</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Barbu, Tudor</contributor><contributor>Tudor Barbu</contributor><creatorcontrib>Wang, Hui</creatorcontrib><creatorcontrib>Zhang, Chenglei</creatorcontrib><creatorcontrib>Yin, Xiyan</creatorcontrib><creatorcontrib>Zhao, Feiyu</creatorcontrib><creatorcontrib>Sheng, Buyun</creatorcontrib><creatorcontrib>Huang, Peide</creatorcontrib><title>A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing</title><title>Mathematical problems in engineering</title><description>The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. 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Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. 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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | 3-D printers Algorithms Cloud computing Computer graphics Data transmission Engineering Least squares method Lightweight Mathematical problems On-line systems Pretreatment Product development Rapid prototyping Reconstruction Registration Scanners Scanning Three dimensional models Three dimensional printing Visualization Weight reduction |
| title | A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing |
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