Premium
Hermite Radial Basis Functions Implicits
Author(s) -
Macêdo I.,
Gois J. P.,
Velho L.
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2010.01785.x
Subject(s) - radial basis function , hermite interpolation , hermite polynomials , interpolation (computer graphics) , basis function , basis (linear algebra) , hermite spline , computer science , offset (computer science) , mathematics , surface (topology) , regularization (linguistics) , algorithm , cubic hermite spline , class (philosophy) , thin plate spline , mathematical optimization , nearest neighbor interpolation , multivariate interpolation , artificial intelligence , mathematical analysis , spline interpolation , geometry , computer vision , image (mathematics) , bilinear interpolation , artificial neural network , programming language
The Hermite radial basis functions (HRBF) implicits reconstruct an implicit function which interpolates or approximates scattered multivariate Hermite data (i.e. unstructured points and their corresponding normals). Experiments suggest that HRBF implicits allow the reconstruction of surfaces rich in details and behave better than previous related methods under coarse and/or non‐uniform samplings, even in the presence of close sheets. HRBF implicits theory unifies a recently introduced class of surface reconstruction methods based on radial basis functions (RBF), which incorporate normals directly in their problem formulation. Such class has the advantage of not depending on manufactured offset‐points to ensure existence of a non‐trivial implicit surface RBF interpolant. In fact, we show that HRBF implicits constitute a particular case of Hermite–Birkhoff interpolation with radial basis functions, whose main results we present here. This framework not only allows us to show connections between the present method and others but also enable us to enhance the flexibility of our method by ensuring well‐posedness of an interesting combined interpolation/regularization approach.