I have an implicit scalar field defined in 2D, for every point in 2D I can make it compute an exact scalar value but its a somewhat complex computation.
I would like to draw an iso-line of that surface, say the line of the ‘0’ value. The function itself is continuous but the ‘0’ iso-line can have multiple continuous instances and it is not guaranteed that all of them are connected.
Calculating the value for each pixel is not an option because that would take too much time – in the order of a few seconds and this needs to be as real time as possible.
What I’m currently using is a recursive division of space which can be thought of as a kind of quad-tree. I take an initial, very coarse sampling of the space and if I find a square which contains a transition from positive to negative values, I recursively divide it to 4 smaller squares and checks again, stopping at the pixel level. The positive-negative transition is detected by sampling a sqaure in its 4 corners. This work fairly well, except when it doesn’t. The iso-lines which are drawn sometimes get cut because the transition detection fails for transitions which happen in a small area of an edge and that don’t cross a corner of a square.
Is there a better way to do iso-line drawing in this settings?
I’ve had a lot of success with the algorithms described here http://web.archive.org/web/20140718130446/http://members.bellatlantic.net/~vze2vrva/thesis.html which discuss adaptive contouring (similar to that which you describe), and also some other issues with contour plotting in general.
There is no general way to guarantee finding all the contours of a function, without looking at every pixel. There could be a very small closed contour, where a region only about the size of a pixel where the function is positive, in a region where the function is generally negative. Unless you sample finely enough that you place a sample inside the positive region, there is no general way of knowing that it is there.
If your function is smooth enough, you may be able to guess where such small closed contours lie, because the modulus of the function gets small in a region surrounding them. The sampling could then be refined in these regions only.