... 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 <martin34...@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 > >
_______________________________________________ Powered by www.kitware.com Visit other Kitware open-source projects at http://www.kitware.com/opensource/opensource.html Please keep messages on-topic and check the ParaView Wiki at: http://paraview.org/Wiki/ParaView Search the list archives at: http://markmail.org/search/?q=ParaView Follow this link to subscribe/unsubscribe: http://public.kitware.com/mailman/listinfo/paraview
