Publication
Importance Caching for Complex Illumination
Philipp Slusallek; Iliyan Georgiev; Jaroslav Krivanek; Stefan Popov
In: P. Cignoni; T. Ertl (Hrsg.). Computer Graphics Forum, Vol. 31, Pages 701-710, EUROGRAPHICS, 2012.
Abstract
Realistic rendering requires computing the global illumination in the scene, and Monte Carlo integration is the
best-known method for doing that. The key to good performance is to carefully select the costly integration samples,
which is usually achieved via importance sampling. Unfortunately, visibility is difficult to factor into the importance
distribution, which can greatly increase variance in highly occluded scenes with complex illumination.
In this paper, we present importance caching a novel approach that selects those samples with a distribution that
includes visibility, while maintaining efficiency by exploiting illumination smoothness. At a sparse set of locations
in the scene, we construct and cache several types of probability distributions with respect to a set of virtual point
lights (VPLs), which notably include visibility. Each distribution type is optimized for a specific lighting condition.
For every shading point, we then borrow the distributions from nearby cached locations and use them for VPL
sampling, avoiding additional bias. A novel multiple importance sampling framework finally combines the many
estimators. In highly occluded scenes, where visibility is a major source of variance in the incident radiance, our
approach can reduce variance by more than an order of magnitude. Even in such complex scenes we can obtain
accurate and low noise previews with full global illumination in a couple of seconds on a single mid-range CPU.