{"id":921,"date":"2026-07-22T09:47:02","date_gmt":"2026-07-22T09:47:02","guid":{"rendered":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/meshless-methods-in-computer-graphics-and-visualization\/"},"modified":"2026-07-22T09:47:02","modified_gmt":"2026-07-22T09:47:02","slug":"meshless-methods-in-computer-graphics-and-visualization","status":"publish","type":"post","link":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/en\/meshless-methods-in-computer-graphics-and-visualization\/","title":{"rendered":"Meshless Methods in Computer Graphics and Visualization"},"content":{"rendered":"<p><em>Vaclav Skala<\/em><br \/><small>University of West Bohemia in Pilsen, Plzen<\/small><\/p>\n<p>Meshless Methods in Computer Graphics and Visualization<br \/>\nVaclav Skala<br \/>\nwww.VaclavSkala.eu<br \/>\nFaculty of Applied Sciences, University of West Bohemia, Pilsen<\/p>\n<p>Radial Basis Function (RBF) is a powerful mesh-free technique used for interpolation or approximation of irregularly scattered multi-dimensional spatio-temporal data.<br \/>\nIt is based on a solution of a system of linear equations $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$. <\/p>\n<p>Unlike grid-based techniques (e.g., finite differences or splines), RBFs depend only on the Euclidean distances between data points, making them naturally suited to problems where the sampling domain is non-uniform and the data are irregularly scattered.<\/p>\n<p>Computational complexity is independent of the data dimensionality; it depends only on the number of interpolated values, not on the dimensionality.<\/p>\n<p>The final interpolated values are then given in the analytical form $h= f(\\mathbf{x})$, i.e., $h=f(x_1,\\ldots,x_d)$ and fits the sparse data (acquired, observed, etc.) smoothly.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Vaclav SkalaUniversity of West Bohemia in Pilsen, Plzen Meshless Methods in Computer Graphics and Visualization Vaclav Skala www.VaclavSkala.eu Faculty of Applied Sciences, University of West Bohemia, Pilsen Radial Basis Function (RBF) is a powerful mesh-free technique used for interpolation or approximation of irregularly scattered multi-dimensional spatio-temporal data. It is based on a solution of a [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-921","post","type-post","status-publish","format-standard","hentry","category-presentations"],"_links":{"self":[{"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/posts\/921","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/users\/63"}],"replies":[{"embeddable":true,"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/comments?post=921"}],"version-history":[{"count":0,"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/posts\/921\/revisions"}],"wp:attachment":[{"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/media?parent=921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/categories?post=921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/home.pf.jcu.cz\/~csgg2026\/index.php\/wp-json\/wp\/v2\/tags?post=921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}