[Paraview] Point Dataset Interpolator - EllipsoidalGaussianKernel

[M P]

Hello all,

I am trying to setup this filter to sample a point volume dataset along a
defined spline curve. The ideal "kernel" for my purpose would be a short
cylinder with some relatively big radius and constant weight in the whole
volume of the cylinder (the axis of this cylinder should follow the
direction vector of the spline). From all the kernels available for this
filter the "EllipsoidalGaussianKernel" seems to be the most suitable. But I
have a problem understanding what some of the parameters exactly mean.

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"Use Normals": Specify whether vector values should be used to affect the
shape of the Gaussian distribution.

?: What Normals vector values are meant here? Is it the normal to the
spline or some vector from dataset? And how is then the kernel oriented if
this option is not used?
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"Use Scalars"

?: Similar question, what scalar is meant here and how is the weighting
done? But I guess this is not important for my use case
------
"Sharpness": Specify the sharpness (i.e., falloff) of the Gaussian. By
default Sharpness=2. As the sharpness increases the effects of distant
points are reduced.

?: I would like all the dataset points have the same weight - so I guess I
need maximum sharpness (like 20) - but this parameter help comment indicate
that it works the opposite way - higher value means lower weight of distant
points
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"Eccentricity": Specify the eccentricity of the ellipsoidal Gaussian. A
value=1.0 produces a spherical distribution. Values less than 1 produce a
needle like distribution (in the direction of the normal); values greater
than 1 produce a pancake like distribution (orthogonal to the normal.

?: Here I am confused by the word "normal", should I understand that it
actually means direction vector?
------

Is there some paper or book where this parameters are exactly explained, or
could someone point me to a source code where this is implemented (and
hopefully it would be possible to understand from there)?

Best regards,

Martin
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