A variational approach for feature-aware B-spline curve design on surface meshes
Robust curve design on surface meshes with flexible controls is useful in a wide range of applications but remains challenging. Most existing methods fall into one of the two strategies: one is to discretize a curve into a polyline, which is then optimized, and the other is to directly design smooth...
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Veröffentlicht in: | The Visual computer 2023-08, Vol.39 (8), p.3767-3781 |
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description | Robust curve design on surface meshes with flexible controls is useful in a wide range of applications but remains challenging. Most existing methods fall into one of the two strategies: one is to discretize a curve into a polyline, which is then optimized, and the other is to directly design smooth splines on meshes. While the former approach usually needs a sufficiently dense sampling of curve points, which is computational costly, the latter approach relaxes the sampling requirement but suffers from the lack of user control. To tackle these problems, we proposed a variational method for designing feature-aware B-spline curves on surface meshes. Given the recent advances in shell space construction methods, we could relax the B-spline curve inside a simplified shell mesh and evaluate its distance to the surface using equipped bijective mapping. To effectively minimize the distance between the curve and the surface, with additional controls in the form of both internal and external constraints, we applied the interior point method and adaptively inserted knots of the spline to increase its freedom and adjust the weighting during the iterations. When the curve is close enough to the surface, it can be efficiently sampled at any resolution and robustly projected to the surface. Experiments show that our method is more robust, has higher flexibility, and generates smoother results than existing methods. |
doi_str_mv | 10.1007/s00371-023-03001-x |
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Most existing methods fall into one of the two strategies: one is to discretize a curve into a polyline, which is then optimized, and the other is to directly design smooth splines on meshes. While the former approach usually needs a sufficiently dense sampling of curve points, which is computational costly, the latter approach relaxes the sampling requirement but suffers from the lack of user control. To tackle these problems, we proposed a variational method for designing feature-aware B-spline curves on surface meshes. Given the recent advances in shell space construction methods, we could relax the B-spline curve inside a simplified shell mesh and evaluate its distance to the surface using equipped bijective mapping. To effectively minimize the distance between the curve and the surface, with additional controls in the form of both internal and external constraints, we applied the interior point method and adaptively inserted knots of the spline to increase its freedom and adjust the weighting during the iterations. When the curve is close enough to the surface, it can be efficiently sampled at any resolution and robustly projected to the surface. 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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><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c314t-7beec41aa1f5859fbd34975cc6f55642b65d0cd65f8aa7c2cc99f7887fe81e0f3</cites><orcidid>0000-0002-2902-9662</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/s00371-023-03001-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918136779?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21368,27903,27904,33723,41467,42536,43784,51298,64362,64366,72216</link.rule.ids></links><search><creatorcontrib>Xu, Rongyan</creatorcontrib><creatorcontrib>Jin, Yao</creatorcontrib><creatorcontrib>Zhang, Huaxiong</creatorcontrib><creatorcontrib>Zhang, Yun</creatorcontrib><creatorcontrib>Lai, Yu-kun</creatorcontrib><creatorcontrib>Zhu, Zhe</creatorcontrib><creatorcontrib>Zhang, Fang-Lue</creatorcontrib><title>A variational approach for feature-aware B-spline curve design on surface meshes</title><title>The Visual computer</title><addtitle>Vis Comput</addtitle><description>Robust curve design on surface meshes with flexible controls is useful in a wide range of applications but remains challenging. Most existing methods fall into one of the two strategies: one is to discretize a curve into a polyline, which is then optimized, and the other is to directly design smooth splines on meshes. While the former approach usually needs a sufficiently dense sampling of curve points, which is computational costly, the latter approach relaxes the sampling requirement but suffers from the lack of user control. To tackle these problems, we proposed a variational method for designing feature-aware B-spline curves on surface meshes. Given the recent advances in shell space construction methods, we could relax the B-spline curve inside a simplified shell mesh and evaluate its distance to the surface using equipped bijective mapping. To effectively minimize the distance between the curve and the surface, with additional controls in the form of both internal and external constraints, we applied the interior point method and adaptively inserted knots of the spline to increase its freedom and adjust the weighting during the iterations. When the curve is close enough to the surface, it can be efficiently sampled at any resolution and robustly projected to the surface. Experiments show that our method is more robust, has higher flexibility, and generates smoother results than existing methods.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>B spline functions</subject><subject>Computer Graphics</subject><subject>Computer Science</subject><subject>Design</subject><subject>Energy</subject><subject>Euclidean space</subject><subject>Image Processing and Computer Vision</subject><subject>Knots</subject><subject>Methods</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Partial differential equations</subject><subject>Robustness</subject><subject>Sampling</subject><issn>0178-2789</issn><issn>1432-2315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kDtPwzAUhS0EEqXwB5gsMRv8iGN7LBUvqRIMMFuuc92mapNgJ6X8ewxBYmO6y3eOzv0QumT0mlGqbhKlQjFCuSBUUMrI4QhNWCE44YLJYzShTGnClTan6CylTUaUKswEvczw3sXa9XXbuC12XRdb59c4tBEHcP0QgbgPFwHfktRt6wawH-IecAWpXjW4bXAaYnAe8A7SGtI5Oglum-Di907R2_3d6_yRLJ4fnuazBfGCFT1RSwBfMOdYkFqasKxEYZT0vgxSlgVflrKivipl0M4pz703JiitVQDNgAYxRVdjbx78PkDq7aYdYv4hWW6YZqJUymSKj5SPbUoRgu1ivXPx0zJqv83Z0ZzN5uyPOXvIITGGUoabFcS_6n9SX77ociw</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Xu, Rongyan</creator><creator>Jin, Yao</creator><creator>Zhang, Huaxiong</creator><creator>Zhang, Yun</creator><creator>Lai, Yu-kun</creator><creator>Zhu, Zhe</creator><creator>Zhang, Fang-Lue</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-2902-9662</orcidid></search><sort><creationdate>20230801</creationdate><title>A variational approach for feature-aware B-spline curve design on surface meshes</title><author>Xu, Rongyan ; Jin, Yao ; Zhang, Huaxiong ; Zhang, Yun ; Lai, Yu-kun ; Zhu, Zhe ; Zhang, Fang-Lue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c314t-7beec41aa1f5859fbd34975cc6f55642b65d0cd65f8aa7c2cc99f7887fe81e0f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>B spline functions</topic><topic>Computer Graphics</topic><topic>Computer Science</topic><topic>Design</topic><topic>Energy</topic><topic>Euclidean space</topic><topic>Image Processing and Computer Vision</topic><topic>Knots</topic><topic>Methods</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Partial differential equations</topic><topic>Robustness</topic><topic>Sampling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Rongyan</creatorcontrib><creatorcontrib>Jin, Yao</creatorcontrib><creatorcontrib>Zhang, Huaxiong</creatorcontrib><creatorcontrib>Zhang, Yun</creatorcontrib><creatorcontrib>Lai, Yu-kun</creatorcontrib><creatorcontrib>Zhu, Zhe</creatorcontrib><creatorcontrib>Zhang, Fang-Lue</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>The Visual computer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Rongyan</au><au>Jin, Yao</au><au>Zhang, Huaxiong</au><au>Zhang, Yun</au><au>Lai, Yu-kun</au><au>Zhu, Zhe</au><au>Zhang, Fang-Lue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A variational approach for feature-aware B-spline curve design on surface meshes</atitle><jtitle>The Visual computer</jtitle><stitle>Vis Comput</stitle><date>2023-08-01</date><risdate>2023</risdate><volume>39</volume><issue>8</issue><spage>3767</spage><epage>3781</epage><pages>3767-3781</pages><issn>0178-2789</issn><eissn>1432-2315</eissn><abstract>Robust curve design on surface meshes with flexible controls is useful in a wide range of applications but remains challenging. Most existing methods fall into one of the two strategies: one is to discretize a curve into a polyline, which is then optimized, and the other is to directly design smooth splines on meshes. While the former approach usually needs a sufficiently dense sampling of curve points, which is computational costly, the latter approach relaxes the sampling requirement but suffers from the lack of user control. To tackle these problems, we proposed a variational method for designing feature-aware B-spline curves on surface meshes. Given the recent advances in shell space construction methods, we could relax the B-spline curve inside a simplified shell mesh and evaluate its distance to the surface using equipped bijective mapping. To effectively minimize the distance between the curve and the surface, with additional controls in the form of both internal and external constraints, we applied the interior point method and adaptively inserted knots of the spline to increase its freedom and adjust the weighting during the iterations. When the curve is close enough to the surface, it can be efficiently sampled at any resolution and robustly projected to the surface. Experiments show that our method is more robust, has higher flexibility, and generates smoother results than existing methods.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00371-023-03001-x</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-2902-9662</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Artificial Intelligence B spline functions Computer Graphics Computer Science Design Energy Euclidean space Image Processing and Computer Vision Knots Methods Optimization Original Article Partial differential equations Robustness Sampling |
title | A variational approach for feature-aware B-spline curve design on surface meshes |
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