> Consider when the algorithm places a point <i>p</i> and then samples its annulus to get a new point <i>q</i>.<p>I was confused for a while thinking <i>p</i> and <i>q</i> were swapped here, relative to the visualization below. [0] However I now think what I missed is that that the visualization is showing two points that are already firmly-established, and the question is where a potential third (unseen, unnamed) point could be placed.<p>So metaphorically speaking, it's about picking a new direction of travel that isn't guaranteed to be into your own recent footsteps.<p>[0] You might say I have problems minding my p's and q's.
Still one of the most satisfying debug UIs I ever came up with.<p><a href="https://akkartik.name/post/2023-11-04-devlog" rel="nofollow">https://akkartik.name/post/2023-11-04-devlog</a>
Folks may find <a href="https://observablehq.com/@fil/poisson-distribution-generators" rel="nofollow">https://observablehq.com/@fil/poisson-distribution-generator...</a> useful
Possibly interesting post from Casey Muratori, regarding random placement of grass in games: <a href="https://caseymuratori.com/blog_0013" rel="nofollow">https://caseymuratori.com/blog_0013</a>, using blue noise.
Also Casey, but his much cooler/deterministic solution to grass placement, to avoid lines<p><a href="https://caseymuratori.com/blog_0011" rel="nofollow">https://caseymuratori.com/blog_0011</a>
I see the generated points often form lines which would cause aliasing in computer graphics, why not use low discrepancy sequences instead?
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.<p>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.
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
Oh, that’s rather a different sort of disk sampling than I imagined.
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