| dc.description.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. | en_US |
