We present Diffusion Restore, a real-time framework for diffusion-based MCMC light transport. MCMC methods are highly suitable for sampling from complex high-dimensional distributions and for approximating integrals over them. In practice, they are often the only viable solution when direct sampling is not possible and alternative methods are either inefficient or cannot be applied due to the structure of the target distribution. However, controlling the exploration of the target distribution in MCMC methods remains challenging. Efficient exploration requires a balance between local exploration and global discovery, and local dynamics must rapidly explore individual modes without getting stuck or exhibiting excessive backtracking. The problem of global discovery has recently been addressed by the introduction of the Restore framework. In this work, we build on this framework and focus on improving local exploration. We show how to choose diffusion-based local dynamics within the Restore framework while completely avoiding Metropolis-adjustment, which is known to slow down convergence. Furthermore, we model these dynamics as nonreversible, introducing momentum in the drift and thereby enabling more directed exploration of the target distribution compared to reversible, random-walk-like dynamics. We provide a theoretical justification for the validity of our choice of local dynamics. Empirically, we demonstrate across diverse scenes that Diffusion Restore outperforms all existing MCMC light transport methods and establishes a new state of the art. In addition, we present a GPU implementation in ray tracing and compute shaders and achieve real-time frame rates. This demonstrates that Diffusion Restore is not only superior in offline rendering, but also outperforms traditional Path Tracing methods in real-time rendering settings, such as interactive applications and games.

翻译：我们提出了扩散修复，一种基于扩散的马尔可夫链蒙特卡罗光传输实时框架。马尔可夫链蒙特卡罗方法非常适用于从复杂高维分布中采样以及近似其上的积分。在实践中，当直接采样不可行且替代方法效率低下或因目标分布结构而无法应用时，这些方法通常是唯一可行的解决方案。然而，在马尔可夫链蒙特卡罗方法中控制目标分布的探索仍然具有挑战性。高效探索需要在局部勘探与全局发现之间取得平衡，且局部动力学必须快速探索各个模式，避免陷入停滞或出现过度回溯。全局发现问题已由修复框架的引入得到初步解决。本文基于该框架，重点改进局部探索。我们展示了如何在修复框架内选择基于扩散的局部动力学，同时完全避免会减慢收敛速度的Metropolis调整。此外，我们将这些动力学建模为非可逆过程，在漂移中引入动量，从而相较于可逆的随机游走式动力学，实现了对目标分布更具方向性的探索。我们为所选局部动力学的有效性提供了理论证明。在实证方面，我们通过多种场景证明，扩散修复优于所有现有马尔可夫链蒙特卡罗光传输方法，并确立了新的最优性能。此外，我们在光线追踪和计算着色器中实现了GPU版本，并达到了实时帧率。这表明扩散修复不仅在后端渲染中表现卓越，还在交互式应用和游戏等实时渲染场景中优于传统路径追踪方法。