3 ms·
If simplex noise covers a larger range, why not figure out the relationship between input->output range (or standard deviation), and apply that as a normalisati
by MillenialMan 5y ago
If simplex noise covers a larger range, why not figure out the relationship between input->output range (or standard deviation), and apply that as a normalisation to the inputs? I assume the relationship is predictable, and tuning the inputs wouldn't increase computational complexity. The differing ranges make it hard to compare and hard to see the different artifacts.
- Rexxar 5y agoIndeed, the comparisons seems meaningless to me. Having play a little with Perlin noise I just see samples a different scales/ranges and not anything fundamentally different.
- bit-101 5y agoAuthor of the post here. I agree. It's not a big deal. I just wanted to compare the two and document my findings. As I said in the post, Simplex isn't a drop in replacement for Perlin, but it's easy enough to tweak the values to match if that's what you need to do. Most applications of Perlin noise (from what I can see) are more on the creative side of things, and in most cases, someone is just fiddling with some values until something "looks right" anyway.