In this paper, we present a neural path guiding method to aid with Monte Carlo (MC) integration in rendering. Existing neural methods utilize distribution representations that are either fast or expressive, but not both. We propose a simple, but effective, representation that is sufficiently expressive and reasonably fast. Specifically, we break down the 2D distribution over the directional domain into two 1D probability distribution functions (PDF). We propose to model each 1D PDF using a neural network that estimates the distribution at a set of discrete coordinates. The PDF at an arbitrary location can then be evaluated and sampled through interpolation. To train the network, we maximize the similarity of the learned and target distributions. To reduce the variance of the gradient during optimizations and estimate the normalization factor, we propose to cache the incoming radiance using an additional network. Through extensive experiments, we demonstrate that our approach is better than the existing methods, particularly in challenging scenes with complex light transport.

This project was funded in part by the NSF CAREER Award #2238193. We are grateful to [Dong et al. 2023] for releasing the source code of their work. We would like to thank the following artists for sharing their scenes and models that appear in our figures: Mareck (Bathroom), SlykDrako (Bedroom), Wig42 (Breakfast, Staircase), Jay-Artist (Kitchen), nacimus (Salle de Bain), Benedikt Bitterli (Veach Door}, Cornell Box) and CN Entertainment, LordSamueliSolo (Swimming Pool).