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Modeling range images with bounded error triangular meshes without optimization
Presents a technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a refer...
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creator | Sappa, A.D. Garcia, M.A. |
description | Presents a technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a reference frame associated with the range sensor. Then, those 3D points are mapped to a 3D curvature space. In the second stage, the points contained in this curvature space are triangulated through a 3D Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this way, successive fronts of triangular meshes are obtained in both range image space and curvature space. This iterative process is applied until a triangular mesh in the range image space fulfilling the given approximation error is obtained. Experimental results are presented. |
doi_str_mv | 10.1109/ICPR.2000.905360 |
format | conference_proceeding |
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This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a reference frame associated with the range sensor. Then, those 3D points are mapped to a 3D curvature space. In the second stage, the points contained in this curvature space are triangulated through a 3D Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this way, successive fronts of triangular meshes are obtained in both range image space and curvature space. This iterative process is applied until a triangular mesh in the range image space fulfilling the given approximation error is obtained. Experimental results are presented.</description><identifier>ISSN: 1051-4651</identifier><identifier>ISBN: 0769507506</identifier><identifier>ISBN: 9780769507507</identifier><identifier>EISSN: 2831-7475</identifier><identifier>DOI: 10.1109/ICPR.2000.905360</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acceleration ; Application software ; Approximation error ; Computer errors ; Computer science ; Computer vision ; Image sensors ; Mesh generation ; Optimization methods ; Pixel</subject><ispartof>Proceedings 15th International Conference on Pattern Recognition. 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ICPR-2000</title><addtitle>ICPR</addtitle><description>Presents a technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a reference frame associated with the range sensor. Then, those 3D points are mapped to a 3D curvature space. In the second stage, the points contained in this curvature space are triangulated through a 3D Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this way, successive fronts of triangular meshes are obtained in both range image space and curvature space. This iterative process is applied until a triangular mesh in the range image space fulfilling the given approximation error is obtained. Experimental results are presented.</description><subject>Acceleration</subject><subject>Application software</subject><subject>Approximation error</subject><subject>Computer errors</subject><subject>Computer science</subject><subject>Computer vision</subject><subject>Image sensors</subject><subject>Mesh generation</subject><subject>Optimization methods</subject><subject>Pixel</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>0769507506</isbn><isbn>9780769507507</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNp9jjsLwjAURi8-wPrYxSl_oPWmbVo7i6KDKOIukV7bSNtIkiL66xV0djrDd_g4AFOOAeeYzbfLwzEIETHIUEQJdsALFxH30zgVXRhimmQCU4FJDzyOgvtxIvgAhtbeEEOMxMKD_U7nVKmmYEY2BTFVy4IseyhXsotum5xyRsZow5xRH6OtpGE12fIn6dYxfXeqVi_plG7G0L_KytLkxxHM1qvTcuMrIjrfzeffPM_f3ujv-AYV8UHU</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Sappa, A.D.</creator><creator>Garcia, M.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2000</creationdate><title>Modeling range images with bounded error triangular meshes without optimization</title><author>Sappa, A.D. ; Garcia, M.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_9053603</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Acceleration</topic><topic>Application software</topic><topic>Approximation error</topic><topic>Computer errors</topic><topic>Computer science</topic><topic>Computer vision</topic><topic>Image sensors</topic><topic>Mesh generation</topic><topic>Optimization methods</topic><topic>Pixel</topic><toplevel>online_resources</toplevel><creatorcontrib>Sappa, A.D.</creatorcontrib><creatorcontrib>Garcia, M.A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sappa, A.D.</au><au>Garcia, M.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Modeling range images with bounded error triangular meshes without optimization</atitle><btitle>Proceedings 15th International Conference on Pattern Recognition. ICPR-2000</btitle><stitle>ICPR</stitle><date>2000</date><risdate>2000</risdate><volume>1</volume><spage>392</spage><epage>395 vol.1</epage><pages>392-395 vol.1</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>0769507506</isbn><isbn>9780769507507</isbn><abstract>Presents a technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a reference frame associated with the range sensor. Then, those 3D points are mapped to a 3D curvature space. In the second stage, the points contained in this curvature space are triangulated through a 3D Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this way, successive fronts of triangular meshes are obtained in both range image space and curvature space. This iterative process is applied until a triangular mesh in the range image space fulfilling the given approximation error is obtained. Experimental results are presented.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2000.905360</doi></addata></record> |
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ispartof | Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000, Vol.1, p.392-395 vol.1 |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Acceleration Application software Approximation error Computer errors Computer science Computer vision Image sensors Mesh generation Optimization methods Pixel |
title | Modeling range images with bounded error triangular meshes without optimization |
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