Photon-Driven Neural Path Guiding
Shilin Zhu, Zexiang Xu, Tiancheng Sun, Alexandr Kuznetsov, Mark Meyer,, Henrik Wann Jensen, Hao Su, Ravi Ramamoorthi

TL;DR
This paper introduces a neural path guiding method that reconstructs high-quality importance sampling distributions from sparse data using an offline-trained neural network, significantly improving rendering efficiency in complex scenes.
Contribution
It presents a novel photon-driven neural path guiding approach that leverages scene partitioning and neural networks to enhance importance sampling in path tracing from limited samples.
Findings
Achieves better rendering quality than previous methods.
Generalizes well to unseen challenging scenes.
Efficiently reconstructs sampling distributions for any scene region.
Abstract
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…
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Taxonomy
TopicsOptical Imaging and Spectroscopy Techniques · Cell Image Analysis Techniques · Neural dynamics and brain function
