Fast, discrete natural neighbor interpolation in 3D on a CPU.
Project description
Discrete Sibson (Natural Neighbor) Interpolation
Natural neighbor interpolation is a method for interpolating scattered data (i.e. you know the values of a function at scattered locations). It is often superior to linear barycentric interpolation, which is a commonly used method of interpolation provided by Scipy’s griddata function.
There are several implementations of 2D natural neighbor interpolation in Python. We needed a fast 3D implementation that could run without a GPU, so we wrote an implementation of Discrete Sibson Interpolation (a version of natural neighbor interpolation that is fast but introduces slight errors as compared to “geometric” natural neighbor interpolation).
See https://doi.org/10.1109/TVCG.2006.27 for details.
Dependencies
Python 3.4+
Numpy (has been tested with 1.13+)
Demonstration
Natural neighbor interpolation can be more accurate than linear barycentric interpolation (Scipy’s default) for smoothly varying functions.
Also, the final result looks better.
Future Work
Provide options for extrapolation handling
Support floats and complex numbers (only support doubles at the moment)
Support 2D (only support 3D)
Add documentation with discussion on limitations of discrete sibson’s method
Uncomment cpplint from tox.ini and cleanup C++ code
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