I love these kinds of problems, because they try to produce what humans perceive as random instead of something truly random. Another great example of this is blue noise
For a low-discrepancy sequence you are usually trying to generate one point at a time, up to some arbitrary number. Here the goal is to generate (roughly) a specific number of points that fill a whole region.
So you probably could figure out a way to use this method to make a low-discrepancy sequence but it's probably not going to be particularly suitable compared to alternatives.
[delayed]
Still one of the most satisfying debug UIs I ever came up with.
https://akkartik.name/post/2023-11-04-devlog
Folks may find https://observablehq.com/@fil/poisson-distribution-generator... useful
Also fun: https://observablehq.com/@jrus/spheredisksample
Possibly interesting post from Casey Muratori, regarding random placement of grass in games: https://caseymuratori.com/blog_0013, using blue noise.
Also Casey, but his much cooler/deterministic solution to grass placement, to avoid lines
https://caseymuratori.com/blog_0011
I see the generated points often form lines which would cause aliasing in computer graphics, why not use low discrepancy sequences instead?
Oh, that’s rather a different sort of disk sampling than I imagined.
I love these kinds of problems, because they try to produce what humans perceive as random instead of something truly random. Another great example of this is blue noise
I'm wondering if it can be used as a low-discrepancy sequence
For a low-discrepancy sequence you are usually trying to generate one point at a time, up to some arbitrary number. Here the goal is to generate (roughly) a specific number of points that fill a whole region.
So you probably could figure out a way to use this method to make a low-discrepancy sequence but it's probably not going to be particularly suitable compared to alternatives.