A Data-Driven Approach to Analytical Dwivedi Guiding

Abstract

Path tracing remains the gold standard for high-fidelity subsurface scattering despite requiring numerous paths for noise-free estimates. We introduce a novel variance-reduction method based on two complementary zero-variance-theory-based approaches. The first one, analytical Dwivedi sampling, is lightweight but struggles with complex lighting. The second one, surface path guiding, learns incident illumination at boundaries to guide sampled paths, but it does not reduce variance from subsurface scattering. In our novel method, we enhance Dwivedi sampling by incorporating the radiance field learned only at the volume boundary. We use the average normal of points on an illuminated boundary region or directions sampled from distributions of incident light at the boundary as our analytical Dwivedi slab normals. Unlike previous methods based on Dwivedi sampling, our method is efficient even in scenes with complex light rigs typical for movie production and under indirect illumination. We achieve comparable noise reduction and even slightly improved estimates in some scenes compared to volume path guiding, and our method can be easily added on top of any existing surface path guiding system. Our method is particularly effective for homogeneous, isotropic media, bypassing the extensive training and caching inside the 3D volume that volume path guiding requires.