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Diffusion Restore，一个用于基于扩散的MCMC光传输的实时框架。MCMC方法非常适用于从复杂高维分布中采样以及近似计算其积分。在实践中，当直接采样不可行，且替代方法因目标分布结构而效率低下或无法应用时，MCMC通常是唯一可行的解决方案。然而，在MCMC方法中控制对目标分布的探索仍然具有挑战性。高效探索需要在局部探索与全局发现之间取得平衡，且局部动态必须快速探索各个模态，避免陷入停滞或出现过度回溯。全局发现的问题最近已通过Restore框架的引入得到解决。在本工作中，我们基于该框架，专注于改进局部探索。我们展示了如何在Restore框架内选择基于扩散的局部动态，同时完全避免会减慢收敛速度的Metropolis调整。此外，我们将这些动态建模为不可逆的，在漂移中引入动量，从而相较于可逆的随机游走式动态，能够更定向地探索目标分布。我们为所选局部动态的有效性提供了理论依据。实验方面，我们在多种场景中证明，Diffusion Restore优于所有现有MCMC光传输方法，并确立了新的最优水平。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Diffusion Restore不仅在离线渲染中表现卓越，在实时渲染环境中（如交互式应用和游戏），也优于传统路径追踪方法。