Importance sampling of many lights with adaptive tree splitting
Alejandro Conty Estevez, Christopher Kulla · 2017
We present a technique to importance sample large collections of lights. A bounding volume hierarchy over all lights is traversed at each shading point using a single random number in a way that importance samples their predicted contribution. We further improve the performance of the algorithm by forcing splitting until the importance of a cluster is sufficiently representative of its contents.