FreNBRDF: A Frequency-Rectified Neural Material Representation
This work addresses a specific bottleneck in neural material modeling for computer graphics, offering incremental improvements in fidelity and interpretability.
The paper tackled the problem of poor frequency-domain behavior in neural material representations for photorealistic rendering by introducing FreNBRDF, a frequency-rectified neural material representation, which improved accuracy and robustness in material appearance reconstruction and editing compared to state-of-the-art baselines.
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 FreNBRDF 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.