FreNBRDF: A Frequency-Rectified Neural Material Representation

2025-07-01

AI Summary

This paper addresses the problem of appearance reconstruction artifacts and poor editing robustness in neural material representations due to insufficient frequency-domain modeling. To this end, we propose a frequency-aware implicit neural material representation. Our method introduces (1) a spherical harmonic–based frequency-domain analysis mechanism to explicitly model the anisotropic spectral characteristics of BRDFs, and (2) a frequency-correction loss function that jointly optimizes low-frequency structural fidelity and high-frequency detail preservation. The resulting representation retains full differentiability and editability while significantly improving material reconstruction accuracy and generalization across varying lighting conditions and viewing angles. Experiments on multiple benchmark datasets demonstrate superior performance over state-of-the-art methods. Moreover, our approach exhibits enhanced controllability and interpretability in downstream tasks such as material editing and relighting.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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 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.

Problem

• Understanding neural material frequency domain behavior
• Improving material appearance reconstruction accuracy
• Enhancing adaptability in BRDF modeling

Innovation

• Frequency-rectified neural material representation
• Spherical harmonics for frequency-domain integration
• Novel frequency-rectified loss for enhanced fidelity