一种将渲染误差高效转化为蓝噪声的过滤方法

基于蒙特卡洛的积分器是最为常用的辐射度近似方法。将蒙特卡洛积分的误差在屏幕空间中分布为蓝噪声，可以显著提升视觉质量。然而，在任意样本数和维度下，对于复杂场景，快速地将积分噪声分散为蓝噪声还难以实现，本文提出了一种简单有效的方法以解决这个问题。首先，使用蓝噪声蒙版扰乱积分的采样序列，使积分在低采样数时具备蓝噪声特性。然后，对渲染结果进行过滤，使渲染结果在更高采样数时也基本保持蓝噪声的视觉效果。实验对比了最新的相关工作，本文的方法在多种不同类型的场景中，都可以快速得到更加稳定的蓝噪声分布的误差。提出的方法不仅在任意样本数和维度的情况下能够基本保持蓝噪声特征，而且计算效率高，适用于现代基于蒙特卡洛方法的渲染器中。

Filtering approach for efficiently distributing render errors as a blue-noise

The Monte Carlo(MC) integrators are the most commonly used approaches for numerically approximating radiant light energy propagating in virtual scenes. Distributing the Monte Carlo errors as blue-noise in the screen space significantly improves the visual fidelity. However, it is difficult to achieve such a goal efficiently for complex scenes with arbitrary sample counts and integration dimensionalities.A simple yet effective approach is proposed to deal with this problem. Firstly, blue-noise masks are adopted to scramble the quasi-random sequences, such that the rendering errors have a blue-noise spectrum at low sampling counts. Then, a filtering method is introduced, and the consequent rendering errors maintain the blue-noise spectrum at higher sample counts. Compared with several related works, the proposed method can efficiently obtain more stable blue noise results in different virtual scenes.The proposed method not only produces stable blue-noise errors but also has high computational efficiency, which is suitable for modern Monte Carlo-based renderers.