FreNBRDF: A Frequency-Rectified Neural Material Representation
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.
68656c2a554cb97d1ed72822
Chenliang Zhou
68656c2a554cb97d1ed72823
Zheyuan Hu
claimed_verified
2025-07-03T07:17:15.081Z
653053c6657ae56cdb5c490b
https://cdn-avatars.huggingface.co/v1/production/uploads/653053c6657ae56cdb5c490b/Zl0bZZ7-tD9AR6muE8esU.png
Peter Hu
user
Peter2023HuggingFace
68656c2a554cb97d1ed72824
Cengiz Oztireli
FreNBRDF: A Frequency-Rectified Neural Material Representation
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.
FreNBRDF, a frequency-rectified neural material representation using spherical harmonics, enhances the accuracy and robustness of material appearance reconstruction and editing.
implicit neural representations BRDF frequency domain spherical harmonics frequency-rectified loss reconstruction editing photorealistic rendering