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.

翻译：蒙特卡洛路径追踪是一种用于逼真图像合成的强大技术，但在低采样数下存在严重噪声，限制了其在实时应用中的使用。为解决这一问题，我们提出了一种端到端训练的框架，包含采样重要性网络、潜空间编码器网络和降噪器网络。我们的方法通过强化学习优化采样重要性网络，从而避免显式数值梯度近似。该方法不通过平均聚集每个像素的采样值，而是保留所有采样值并输入潜空间编码器。编码器通过潜空间中的学习表示替代手工制作的时空启发式规则。最后，训练神经降噪器以优化输出图像。在多个具有挑战性的数据集上，我们的方法提升了视觉质量，并在同等质量下将渲染时间相比先前最先进技术降低1.6倍，使其成为实时应用领域有前景的解决方案。