We present a technique to importance sample large collections of lights (including mesh lights as collections of small emitters) in the context of Monte-Carlo path tracing. A bounding volume hierarchy over all emitters is traversed at each shading point using a single random number in a way that importance samples their predicted contribution. The tree aggregates energy, spatial and orientation information from the emitters to enable accurate prediction of the effect of a cluster of lights on any given shading point. We further improve the performance of the algorithm by forcing splitting until the importance of a cluster is sufficiently representative of its contents.
We present a technique to hide the abrupt shadow terminator line when strong bump or normal maps are used to emulate micro-geometry. Our approach, based on microfacet shadowing functions, is simple and inexpensive. Instead of rendering detailed and expensive height-field shadows, we apply a statistical solution built on the assumption that normals follow a nearly normal random distribution. We also contribute a useful approximate variance measure for GGX, which is otherwise undefined analytically.
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