DNF-Intrinsic: Deterministic Noise-Free Diffusion for Indoor Inverse Rendering
Journal:
arXiv
Published Date:
Jul 5, 2025
Abstract
Recent methods have shown that pre-trained diffusion models can be fine-tuned
to enable generative inverse rendering by learning image-conditioned
noise-to-intrinsic mapping. Despite their remarkable progress, they struggle to
robustly produce high-quality results as the noise-to-intrinsic paradigm
essentially utilizes noisy images with deteriorated structure and appearance
for intrinsic prediction, while it is common knowledge that structure and
appearance information in an image are crucial for inverse rendering. To
address this issue, we present DNF-Intrinsic, a robust yet efficient inverse
rendering approach fine-tuned from a pre-trained diffusion model, where we
propose to take the source image rather than Gaussian noise as input to
directly predict deterministic intrinsic properties via flow matching.
Moreover, we design a generative renderer to constrain that the predicted
intrinsic properties are physically faithful to the source image. Experiments
on both synthetic and real-world datasets show that our method clearly
outperforms existing state-of-the-art methods.
