RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing

Published: 21 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: computer graphics, rendering, ray tracing, GPU acceleration, RL, spatiotemporal latent space
TL;DR: We make ray tracing rendering faster: we encode more efficiently previously observed pixel values using neural networks instead of heuristics; we use RL to choose where to sample light rays.
Abstract: Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time applications. To address this, we propose a framework with end-to-end training of a sampling importance network, a latent space encoder network, and a denoiser network. Our approach uses reinforcement learning to optimize the sampling importance network, thus avoiding explicit numerically approximated gradients. Our method does not aggregate the sampled values per pixel by averaging but keeps all sampled values which are then fed into the latent space encoder. The encoder replaces handcrafted spatiotemporal heuristics by learned representations in a latent space. Finally, a neural denoiser is trained to refine the output image. Our approach increases visual quality on several challenging datasets and reduces rendering times for equal quality by a factor of 1.6x compared to the previous state-of-the-art, making it a promising solution for real-time applications.
Supplementary Material: zip
Submission Number: 4373