Adaptive supersampling is different from ordinary supersampling in the sense that samples are choosen adaptively. It is a recursive method that collects more samples at areas with rapid transitions.
The advantage with this method that makes it more attractive than ordinary supersampling is that we can perform supersampling only at areas where it is needed. However, the recursion itself may cause overhead, so there is a trade-off between the cost of the associated nested sampling method and the cost of the adaptive recursion.
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