Transfinite mean value interpolation over polygons

06/19/2019
by   Michael S. Floater, et al.
0

Mean value interpolation is a method for fitting a smooth function to piecewise-linear data prescribed on the boundary of a polygon of arbitrary shape, and has applications in computer graphics and curve and surface modelling. The method generalizes to transfinite interpolation, i.e., to any continuous data on the boundary but a mathematical proof that interpolation always holds has so far been missing. The purpose of this note is to complete this gap in the theory.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/29/2021

Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation

In the past decade the mathematical theory of machine learning has lagge...
research
01/21/2021

Positive Geometries for Barycentric Interpolation

We propose a novel theoretical framework for barycentric interpolation, ...
research
04/14/2021

Five Degree-of-Freedom Property Interpolation of Arbitrary Grain Boundaries via Voronoi Fundamental Zone Octonion Framework

We introduce the Voronoi fundamental zone octonion interpolation framewo...
research
07/01/2022

Smooth Pycnophylactic Interpolation Produced by Density-Equalising Map Projections

A large amount of quantitative geospatial data are collected and aggrega...
research
02/19/2019

Interpolation of scattered data in R^3 using minimum L_p-norm networks, 1<p<∞

We consider the extremal problem of interpolation of scattered data in R...
research
06/24/2018

Golden interpolation

For the classic aesthetic interpolation problem, we propose a novel thou...

Please sign up or login with your details

Forgot password? Click here to reset