Guiding Light Trees for Many-Light Direct Illumination

Abstract

Path guiding techniques reduce the variance in path tracing by reusing knowledge from previous samples to build adaptive sampling distributions. The Practical Path Guiding (PPG) approach stores and iteratively refines an approximation of the incident radiance field in a spatio-directional data structure that allows sampling the incident radiance. However, due to the limited resolution in both spatial and directional dimensions, this discrete approximation is not able to accurately capture a large number of very small lights. We present an emitter sampling technique to guide next event estimation (NEE) with a global light tree and adaptive tree cuts that integrates into the PPG framework. In scenes with many lights our technique significantly reduces the RMSE compared to PPG with uniform NEE, while adding close to no overhead in scenes with few light sources. The results show that our technique can also aid the incident radiance learning of PPG in scenes with difficult visibility.