Product path guiding with semi-adaptive spatio-directional tree

Abstract

Monte Carlo integration is an established method in simulating light transport. However, due to its stochastic nature, it requires copious amounts of samples to eliminate the estimation error, and variance reduction techniques such as importance sampling are used to improve the convergence speed as a remedy. Path guiding is a class of adaptive importance sampling methods devised specifically for light transport simulation, which demonstrates significant improvements over classical sampling techniques. Yet most of the previous path guiding methods only account for the radiance term, omitting the bidirectional scattering distribution function (BSDF) term. This results in suboptimal sample quality for non-diffuse light transport. This paper presents a path guiding method which guides paths according to the full product of the BSDF and radiance terms in light transport equation. Extending a spatio-directional tree based path guiding method, we use a semi-adaptive grid-quadtree hybrid subdividing the directional domain in a spatial subdomain to learn and represent the radiance field. With the help of BSDF lookup tables used to accelerate BSDF evaluations, this grid-quadtree allows us to efficiently construct approximate product distributions and guide paths according to the product of incident radiance and cosine-weighted BSDF at each path vertex. The resulting method is relatively simple to implement and enables more robust sampling on scenes with many glossy surfaces.