Analytical & Neural approaches to Physically Based Rendering

Path tracing is ubiquitous for photorealistic rendering of various light transport phenomenon. At it’s core, path tracing involves the stochastic evaluation of complex & recursive integrals leading to high computational complexity. Research efforts have thus focused on accelerating path tracing either by improving the stochastic sampling process to achieve better convergence or by using approximate analytical evaluations for a restricted set of these integrals. Another interesting set of research efforts focus on the integration of neural networks within the rendering pipeline, where these networks partially replace stochastic sampling and approximate it’s converged result. The analytic and neural approaches are attractive from an acceleration point of view. Formulated properly & coupled with advances in hardware, these approaches can achieve much better convergence and eventually lead to real-time performance. Motivated by this, we make contributions to both avenues to accelerate path tracing. The first set of efforts aim to reduce the computational effort spent in stochastic direct lighting calculations from area light sources by instead evaluating it analytically. To this end, we introduce the analytic evaluation of visibility in a previously proposed analytic area light shading method. Second, we add support for anisotropic GGX to this method. This relaxes an important assumption enabling the analytic rendering of a wider set of light transport effects. Our final contribution is a neural approach that attempts to reduce yet another source of high computational load - the recursive evaluations. We demonstrate the versatility of our approach with an application to hair rendering, which exhibits one of the most challenging recursive evaluation cases. All our contributions improve on the state-of-the art and demonstrate photo-realism on par with reference path tracing.