FreNBRDF: A Frequency-Rectified Neural Material Representation

TL;DR

FreNBRDF introduces a frequency-aware neural material model using spherical harmonics and a novel loss, significantly improving the accuracy and interpretability of material reconstruction and editing in photorealistic rendering.

Contribution

It presents the first frequency-rectified neural BRDF model that incorporates frequency analysis and spherical harmonics for enhanced material representation.

Findings

Improves accuracy of material appearance reconstruction.

Enhances robustness and interpretability of material editing.

Outperforms state-of-the-art methods in experiments.

Abstract

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.