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I did some simple experiments and fairly swiftly discovered where I went wrong. I'm still not totally convinced that there isn't something clever that can be done for more operations.

My next thought is maybe you can do some interesting shenanigans by jumping to the nearest point on one surface then calculating a modulation that adjusts the distance by an amount. I can certainly see how difficult it would become if you start making convex shapes like that though. There must be a way to take the min of a few candidates within the radius of a less precise envelope surface.



I think you might be describing smoothmin?

https://iquilezles.org/articles/smin/


No I was thinking a hard min, but one that finds a greedy but inaccurate distance and then a refinement takes some samples that measure nearest within a radius. This would handle modulations of the shape where it folded back upon itself as long as they don't fold within the subsample radius.

It's multi sample but selective rather than weighted.




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