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arXiv · 2312.12274

Intrinsic Image Diffusion for Indoor Single-view Material Estimation

Peter Kocsis·Vincent Sitzmann·Matthias Nießner
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Abstract

We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue, we advocate for a probabilistic formulation, where instead of attempting to directly predict the true material properties, we employ a conditional generative model to sample from the solution space. Furthermore, we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by $1.5dB$ on PSNR and by $45\%$ better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

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Peter Kocsis, Vincent Sitzmann, Matthias Nießner. 2023-12-19. Intrinsic Image Diffusion for Indoor Single-view Material Estimation. https://arxiv.org/abs/2312.12274

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