FreNBRDF：一種頻率校正的神經材質表示法

摘要

精確的材料建模對於實現逼真的渲染至關重要，它縮小了計算機生成圖像與真實世界照片之間的差距。傳統方法依賴於表格化的雙向反射分佈函數（BRDF）數據，而近期研究則轉向隱式神經表示，這為多種任務提供了緊湊且靈活的框架。然而，這些方法在頻域中的行為仍鮮為人知。為此，我們引入了FreNBRDF，一種頻率校正的神經材料表示。通過利用球諧函數，我們將頻域考量整合到神經BRDF建模中。我們提出了一種新穎的頻率校正損失函數，該函數源自對神經材料的頻率分析，並將其納入一個可泛化且自適應的重建與編輯流程中。這一框架提升了保真度、適應性和效率。大量實驗表明，與最先進的基線方法相比，FreNBRDF提高了材料外觀重建與編輯的準確性和魯棒性，從而實現了更具結構性和可解釋性的下游任務與應用。

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：一種頻率校正的神經材質表示法

FreNBRDF: A Frequency-Rectified Neural Material Representation

摘要

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