Monte Carlo rendering algorithms often utilize correlations between pixels to improve efficiency and enhance image quality. For real-time applications in particular, repeated reservoir resampling offers a powerful framework to reuse samples both spatially in an image and temporally across multiple frames. While such techniques achieve equal-error up to 100 × faster for real-time direct lighting [5] and global illumination [39, 49], they are still far from optimal. For instance, spatiotemporal resampling often introduces noticeable correlation artifacts, while reservoirs holding more than one sample suffer from impoverishment in the form of duplicate samples. We demonstrate how interleaving Markov Chain Monte Carlo (MCMC) mutations with reservoir resampling helps alleviate these issues, especially in scenes with glossy materials and difficult-to-sample lighting. Moreover, our approach does not introduce any bias, and in practice we find considerable improvement in image quality with just a single mutation per reservoir sample in each frame.

蒙特卡罗渲染算法通常利用像素之间的相关性来提高效率并增强图像质量。特别是对于实时应用，重复的储层重采样提供了一个强大的框架，可以在图像中的空间上和跨多个帧的时间上重复使用样本。虽然此类技术对于实时直接照明 [5] 和全局照明 [39, 49] 的等误差速度提高了 100 倍，但它们仍然远非最佳。例如，时空重采样通常会引入明显的相关伪影，而持有多个样本的水库会因重复样本的形式而遭受贫化。我们演示了如何将马尔可夫链蒙特卡罗 (MCMC)突变与存储库重采样交织在一起有助于缓解这些问题，特别是在具有光泽材质和难以采样的照明的场景中。此外，我们的方法不会引入任何偏差，并且在实践中我们发现图像质量有了相当大的改善，每帧中的每个水库样本仅存在一个突变。