Oh there are lots more axes than just the 3 aliasing, sharpness, and locality ones. Those are the main tradeoffs for a pixel sampling or convolution filter choice, when downscaling an image, say between nearest neighbor vs bilinear vs Mitchell (bicubic). But the antialiasing methods in this article have many tradeoffs that aren’t on the aliasing-sharpness-locality spectrum. Other issues with real time AA methods include bias, correctness, noise, quality, temporal effects, compositing/blending issues, etc. And the topic gets much wider when we start talking about DLSS, we don’t even have established terminology for the many different kinds of tradeoffs neural networks give us. Anyway just noting that the main highlights of discussion in the article, which are MSAA and AAA (and references to TAA and others), don’t fit in the aliasing-sharpness-locality space. MSAA’s tradeoffs include it only running on geometry & texture edges, and its ‘wrong order’ samples or ‘double edges’ noted in the article. TAA has a temporal aspect and is most known for ghosting. AAA as described here doesn’t necessarily blend correctly and in general it can’t handle multiple arbitrary sub-pixel events, it really only works well if there’s one edge crossing a pixel.