Thank you for explaining this better! This is still a bit complicated on the Math side but it’s well illustrated to see the result.
One thing I've been meaning to look into - how to adapt 3D perlin noise to produce gaussian noise - given a specified (scalar) mean and standard deviation.
Why not generate gaussian noise from scratch? By definition it should be indistinguishable.
Pretty and informative!