FreNBRDF: A Frequency-Rectified Neural Material Representation
Authors: name: Chenliang Zhou, name: Zheyuan Hu, name: Cengiz Oztireli

Accurate material modeling is crucial for achieving photorealistic rendering,
bridging the gap between computer-generated imagery and real-world photographs.
While traditional approaches rely on tabulated BRDF data, recent work has
shifted towards implicit neural representations, which offer compact and
flexible frameworks for a range of tasks. However, their behavior in the
frequency domain remains poorly understood. To address this, we introduce
FreNBRDF, a frequency-rectified neural material representation. By leveraging
spherical harmonics, we integrate frequency-domain considerations into neural
BRDF modeling. We propose a novel frequency-rectified loss, derived from a
frequency analysis of neural materials, and incorporate it into a generalizable
and adaptive reconstruction and editing pipeline. This framework enhances
fidelity, adaptability, and efficiency. Extensive experiments demonstrate that
\ours improves the accuracy and robustness of material appearance
reconstruction and editing compared to state-of-the-art baselines, enabling
more structured and interpretable downstream tasks and applications.