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Photon-driven Neural Reconstruction for Path Guiding
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DescriptionWe present a path-guiding approach that can reconstruct high-quality sampling distributions using an offline trained neural network. We leverage photons traced from light sources for sampling density reconstruction, which is effective for challenging scenes with global illumination. We further partition the scene space into an adaptive hierarchical grid.