11–20 of 26 posts
I'm wondering if it can be used as a low-discrepancy sequence
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 was confused for a while thinking p and q 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.
So metaphorically speaking, it's about picking a new direction of travel that isn't guaranteed to be into your own recent footsteps.
[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. https://akkartik.name/post/2023-11-04-devlog
Still one of the most satisfying debug UIs I ever came up with. https://akkartik.name/post/2023-11-04-devlog
thanks, these are great!
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
Never found a way to do this per-pixel in a shader, Bridson's needs the active list. Ended up hashing cells and jittering inside them 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. 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.
All that said, there's definitely been research into samplers that combine low-discrepancy with blue noise properties (often including retaining those properties even in lower-dimensional projections produced by dropping axis).