This is just a post about some changes I made to my personal computers. Probably not interesting for most people, but I like to document this stuff so I can go back and say, “when did I switch to ____?…
Over the last week, I’ve made several deep dives into the subject of noise – Perlin noise, Simplex noise Curl noise, 2d and 3d noise. Worth reading through them in order, if you’re interested in this stuff.
In yesterday’s post, I ran across this statement about Simplex noise: noise generated for different dimensions are visually distinct (e.g. 2D noise has a different look than 2D slices of 3D noise, and it looks increasingly worse for higher dimensions).…
During my deep dive into Perlin and Curl noise, I kept bumping into the subject of Simplex noise. I figured it was worth going down that rabbit hole for a day or so. Here’s what I’ve found.
In my recent post on mapping Perlin noise to angles, I was put on to the subject of Curl noise, which I thought I understood, but did not. I figured out what Curl noise really was in a subsequent post…
I previously had an incorrect concept of what curl noise was. This experiment demonstrates it. Although it isn’t what I thought it was, it’s still a pretty cool concept.
My last post on Perlin noise wound up on hitting Hacker News, which generated an enormous amount of views, and a fair number of comments – here, on Twitter, and on HN itself. Of course, there was the usual eye-rolling,…
A common use case for Perlin Noise is to create some kind of flow field, and a common way to do that is to map the noise value at a particular location to an angle from 0 to PI *…