TAdaptiveSuperSampler

Hierarchy

TPersistent

   |

TNotifiablePersistent

   |

TCustomSampler

   |

TNestedSampler

Description

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.

Reference

Methods Properties Events
In TAdaptiveSuperSampler:
Create Level
GetSample Tolerance
In TNestedSampler:
Sampler
In TCustomSampler:
FinalizeSampling
GetSampleBounds
HasBounds
PrepareSampling
In TNotifiablePersistent:
BeginUpdate UpdateCount OnChange
Changed
Destroy
EndUpdate