Open perrette opened 8 years ago
"The NVidia implementation uses a kernel size of int(sigma*3)."
"You may experiment using a smaller kernel size with higher values of sigma for performance considerations."
I am no sure what you mean... in the example?
I mean in your main code. Look at the figure in 1d. The parameter is r=1. The question is: which cutoff? Non zero values exist until infinity. That's always a problem with convolution. In practice, for x>3 values are close to zero so the true convoluion result (inf) and a cut-off version of it would probably be similar. But if you cutoff at x=1 you will perform something that is quite far from actual gaussian convolution. If all you want is to get some blurring that may be fine, but you may lose other properties...
IMO, rcut = 3×r is a good option.
Hi @sebastianbeyer,
Nice work. I am a bit too lazy to go in the full code for now, but looking at the "naive" example, I see the Gaussian Kernel with
r
parameter is used with a moving window of size2*r
. I would pick at least 2*r, otherwise you cut 1 large chunk of the weights (~1/3). What do you think?