The algorithm also visits children in order of distance to the query point. This means it checks the most promising quadrants first, which tends to find a good candidate early and enables aggressive pruning of the remaining quadrants. Without this ordering, the algorithm would still produce the correct result, but it would prune fewer nodes.
If this sounds familiar to you, it’s because this method works very similarly to error-diffusion. Instead of letting adjacent pixels compensate for the error, we’re letting each successive candidate compensate for the combined error of all previous candidates.
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