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.

通过强化学习优化重要性采样网络，将采样的值输入潜在空间编码器，最终训练一个神经去噪器，该方法在多个数据集上提高了视觉质量，使渲染时间比现有技术提高了1.6倍，成为实时应用的有希望的解决方案。

基于强化学习的状态感知神经自适应采样与去噪方法，用于实时路径追踪