Inverse rendering, the process of inferring scene properties from images, is a challenging inverse problem. The task is ill-posed, as many different scene configurations can give rise to the same image. Most existing solutions incorporate priors into the inverse-rendering pipeline to encourage plausible solutions, but they do not consider the inherent ambiguities and the multi-modal distribution of possible decompositions. In this work, we propose a novel scheme that integrates a denoising diffusion probabilistic model pre-trained on natural illumination maps into an optimization framework involving a differentiable path tracer. The proposed method allows sampling from combinations of illumination and spatially-varying surface materials that are, both, natural and explain the image observations. We further conduct an extensive comparative study of different priors on illumination used in previous work on inverse rendering. Our method excels in recovering materials and producing highly realistic and diverse environment map samples that faithfully explain the illumination of the input images.

逆向渲染是从图像中推断场景属性的挑战性逆向问题。我们提出了一种新颖的方案，将预训练于自然照明图上的降噪扩散概率模型与可微分路径追踪器相结合，允许从自然且能解释图像观察的光照和空间变化的表面材料组合中进行采样，以恢复材料并生成高度逼真和多样化的环境贴图样本。

扩散后照明技术用于模糊感知逆向渲染