FreNBRDF：一种频率校正的神经材质表示方法

摘要

精确的材料建模对于实现逼真渲染至关重要，它弥合了计算机生成图像与真实世界照片之间的差距。传统方法依赖于表格化的双向反射分布函数（BRDF）数据，而近期研究则转向了隐式神经表示，为多种任务提供了紧凑且灵活的框架。然而，这些方法在频域中的行为仍鲜为人知。为此，我们提出了FreNBRDF，一种频率校正的神经材料表示方法。通过利用球谐函数，我们将频域考量融入神经BRDF建模中。我们提出了一种新颖的频率校正损失函数，该函数源自对神经材料的频率分析，并将其整合到一个可泛化且自适应的重建与编辑流程中。这一框架提升了保真度、适应性和效率。大量实验表明，与现有最先进的基线方法相比，\ours显著提高了材料外观重建与编辑的准确性和鲁棒性，使得下游任务和应用更加结构化和可解释。

English

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：一种频率校正的神经材质表示方法

FreNBRDF: A Frequency-Rectified Neural Material Representation

摘要

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