Technical Papers
Selective guided sampling with complete light transport paths
Event Type
Technical Papers
Registration Categories
TimeThursday, 6 December 20182:41pm - 3:07pm
DescriptionFinding good global importance sampling strategies for Monte Carlo light
transport is challenging. While estimators using local methods (such as
BSDF sampling or next event estimation) often work well in the majority
of a scene, small regions in path space can be sampled insufficiently (e.g. a
reflected caustic). We propose a novel data-driven guided sampling method
which selectively adapts to such problematic regions and complements the
unguided estimator. It is based on complete transport paths, i.e. is able to
resolve the correlation due to BSDFs and free flight distances in participating
media. It is conceptually simple and places anisotropic truncated Gaussian
distributions around guide paths to reconstruct a continuous probability
density function (guided PDF). Guide paths are iteratively sampled from the
guided as well as the unguided PDF and only recorded if they cause high
variance in the current estimator. While plain Monte Carlo samples paths
independently and Markov chain-based methods perturb a single current
sample, we determine the reconstruction kernels by a set of neighbouring
paths. This enables local exploration of the integrand without detailed
balance constraints or the need for analytic derivatives. We show that our
method can decompose the path space into a region that is well sampled by
the unguided estimator and one that is handled by the new guided sampler.
In realistic scenarios, we show 4× speedups over the unguided sampler.