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Inverse rendering, the process of inferring scene properties from images, isa challenging inverse problem. The task is ill-posed, as many different sceneconfigurations can give rise to the same image. Most existing solutionsincorporate priors into the inverse-rendering pipeline to encourage plausiblesolutions, but they do not consider the inherent ambiguities and themulti-modal distribution of possible decompositions. In this work, we propose anovel scheme that integrates a denoising diffusion probabilistic modelpre-trained on natural illumination maps into an optimization frameworkinvolving a differentiable path tracer. The proposed method allows samplingfrom combinations of illumination and spatially-varying surface materials thatare, both, natural and explain the image observations. We further conduct anextensive comparative study of different priors on illumination used inprevious work on inverse rendering. Our method excels in recovering materialsand producing highly realistic and diverse environment map samples thatfaithfully explain the illumination of the input images.

Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering