High quality supersampling is about equally hard in 2D and 3D, since the result is 2D. It is also the most common solution in both 2D and 3D graphics, so your i

High quality supersampling is about equally hard in 2D and 3D, since the result is 2D. It is also the most common solution in both 2D and 3D graphics, so your instinct is reasonably good. But, font and path rendering, for example in web browsers and/or with PDF or SVG - these things can benefit in both efficiency and in quality from using analytic antialiasing methods. 2D vector rendering is a place where doing something harder has real payoffs. Just a fun aside - not all supersampling algorithms are equally good. If you use the wrong filter, it's can be very surprising to discover that there are ways you can take a million samples per pixel or more and never succeed in getting rid of aliasing artifacts. (An example is if you just average the samples, aka use a Box Filter.) I have a 2D digital art project to render mathematical functions that can have arbitrarily high frequencies. I spent money making large format prints of them, so I care a lot about getting rid of aliasing problems. I've ended up with a Gaussian filter, which is a tad blurrier than experts tend to like, because everything else ends up giving me visible aliasing somewhere.