Fast and Lightweight Path Guiding Algorithm on GPU

Abstract

We propose a simple, yet practical path guiding algorithm that runs on GPU. Path guiding renders photo-realistic images by simulating the iterative bounces of rays, which are sampled from the radiance distribution. The radiance distribution is often learned by serially updating the hierarchical data structure to represent complex scene geometry, which is not easily implemented with GPU. In contrast, we employ a regular data structure and allow fast updates by processing a significant number of rays with GPU. We further increase the efficiency of radiance learning by employing SARSA [SB18] used in reinforcement learning. SARSA does not include aggregation of incident radiance from all directions nor storing all of the previous paths. The learned distribution is then sampled with an optimized rejection sampling, which adapts the current surface normal to reflect finer geometry than the grid resolution. All of the algorithms have been implemented on GPU using megakernal architecture with NVIDIA OptiX [PBD*10]. Through numerous experiments on complex scenes, we demonstrate that our proposed path guiding algorithm works efficiently on GPU, drastically reducing the number of wasted paths.