Simplex noise vs Perlin noise

I would like to know why Perlin noise is still so popular today after Simplex came out. Simplex noise was made by Ken Perlin himself and it was suppose to take over his old algorithm which was slow for higher dimensions and with better quality (no visible artifacts).

Simplex noise came out in 2001 and over those 10 years I've only seen people talk of Perlin noise when it comes to generating heightmaps for terrains, creating procedural textures, et cetera.

Could anyone help me out, is there some downside of Simplex noise? I heard rumors that Perlin noise is faster when it comes to 1D and 2D noise, but I don't know if it's true or not.

Thanks!

Solution 1:

"If it ain't broke, don't fix it."

See if you can find anyone telling you why Simplex is better. "It's faster and extends to multiple dimensions" and "simplex noise attempts to reduce the complexity of higher dimensional noise functions" were what I found. Most of us work in 2 or 3 dimensions, maybe 4 if we're lucky enough to be doing something with time.

I think its fair to say there is little enough real-time usage of Perlin that is too slow to handle, that for most purposes standard Perlin noise is sufficient. In pre-renderings (such as used in the movie industry) time isn't really important since renderings are slow anyway; and in real-time simulations, we have enough ways to reduce the scope of ongoing processing that it's unlikely you're going to be generating massive noise maps every few nano/milliseconds -- that's just basic real-time optimisation.

Solution 2:

I wouldn't be at all surprised if it was simply because of the name. You have to choose between Perlin noise and Simplex noise. The latter is newer and has some advantages. But, you know, it sounds like the 'simple' version of the two. I'll go with the complexer one; noise is supposed to be complex, isn't it?

People tend to be rather irrational.

Solution 3:

Ken Perlin patented his simplex noise algorithm. His classic algorithm is not patented to my knowledge.

Solution 4:

Some preference for the classic Perlin noise may come from being able to use known values resulting in known visual characteristics, as opposed to investing the time required to find the input parameters needed to get an equivalent output using simplex noise.

[simplex noise] has a slightly different visual character to it, so it’s not always a direct plug-in replacement for classic noise. Applications that depend on the detailed characteristics of classic noise, like the precise feature size, the exact range of values or higher order statistics, might need some modification to look good when using simplex noise instead.
Stefan Gustavson's Simplex noise demystified