Interpolation over a sphere based upon a minimum norm network
Computer Aided Geometric Design - Special issue: Topics in CAGD
Interpolation of scattered data on closed surfaces
Computer Aided Geometric Design
Computer Aided Geometric Design
Interpolation on surfaces using minimum norm networks
Computer Aided Geometric Design
Constant-radius blending of parametric surfaces
Geometric modelling
Modeling surfaces of arbitrary topology using manifolds
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Fitting scattered data on sphere-like surfaces using spherical splines
Journal of Computational and Applied Mathematics - Special issue on scattered data
Scattered data fitting on the sphere
Proceedings of the international conference on Mathematical methods for curves and surfaces II Lillehammer, 1997
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Piecewise Quadratic Approximations on Triangles
ACM Transactions on Mathematical Software (TOMS)
A Multiresolution Tensor Spline Method for Fitting Functions on the Sphere
SIAM Journal on Scientific Computing
A simple manifold-based construction of surfaces of arbitrary smoothness
ACM SIGGRAPH 2004 Papers
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Local hybrid approximation for scattered data fitting with bivariate splines
Computer Aided Geometric Design
Quadratic spherical spline quasi-interpolants on Powell--Sabin partitions
Applied Numerical Mathematics
Construction of spherical spline quasi-interpolants based on blossoming
Journal of Computational and Applied Mathematics
Minimal energy spherical splines on Clough-Tocher triangulations for Hermite interpolation
Applied Numerical Mathematics
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We present C1 methods for either interpolating data or for fitting scattered data associated with a smooth function on a two-dimensional smooth manifold Ω embedded into R3. The methods are based on a local bivariate Powell-Sabin interpolation scheme, and make use of local projections on the tangent planes. The data fitting method is a two-stage method. We illustrate the performance of the algorithms with some numerical examples, which, in particular, confirm the O(h3) order of convergence as the data becomes dense.