Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering
Author(s)
Lyu, Linjie
Tewari, Ayush
Habermann, Marc
Saito, Shunsuke
Zollh?fer, Michael
Leimk?hler, Thomas
Theobalt, Christian
Date Issued
December 4, 2023
Journal
ACM Transactions on Graphics
Publisher
ACM
Citation
Lyu, Linjie, Tewari, Ayush, Habermann, Marc, Saito, Shunsuke, Zollh?fer, Michael et al. 2023. "Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering." ACM Transactions on Graphics, 42 (6).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3618357
