[Paraview] Point Dataset Interpolator - EllipsoidalGaussianKernel

[M P]

... reading it again, the "Sharpness" parameter is clear - for my purpose
of uniform weights it should be as low as possible. But some exact equation
would still be useful.

On Tue, Aug 22, 2017 at 11:36 AM, M P <martin34148 at gmail.com> wrote:

> 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.
>
> ------
> "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?
> ------
> "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
> ------
> "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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