1 research outputs found
Photon-Driven Neural Path Guiding
Although Monte Carlo path tracing is a simple and effective algorithm to
synthesize photo-realistic images, it is often very slow to converge to
noise-free results when involving complex global illumination. One of the most
successful variance-reduction techniques is path guiding, which can learn
better distributions for importance sampling to reduce pixel noise. However,
previous methods require a large number of path samples to achieve reliable
path guiding. We present a novel neural path guiding approach that can
reconstruct high-quality sampling distributions for path guiding from a sparse
set of samples, using an offline trained neural network. We leverage photons
traced from light sources as the input for sampling density reconstruction,
which is highly effective for challenging scenes with strong global
illumination. To fully make use of our deep neural network, we partition the
scene space into an adaptive hierarchical grid, in which we apply our network
to reconstruct high-quality sampling distributions for any local region in the
scene. This allows for highly efficient path guiding for any path bounce at any
location in path tracing. We demonstrate that our photon-driven neural path
guiding method can generalize well on diverse challenging testing scenes that
are not seen in training. Our approach achieves significantly better rendering
results of testing scenes than previous state-of-the-art path guiding methods.Comment: Keywords: computer graphics, rendering, path tracing, path guiding,
machine learning, neural networks, denoising, reconstructio