用神经场建模材质外观，强制符合物理规律提升真实感
Physically Based Neural Bidirectional Reflectance Distribution Function
- 用神经场构建连续材质表示，通过重参数化保证光路可逆
- 结合能量守恒约束，使重建材质在多数据集上视觉质量更优
- 引入色度监督提升色彩准确性，适合高保真渲染应用
我们提出基于物理的神经双向反射分布函数（PBNBRDF），一种基于神经场的连续材质外观表示方法。该模型在准确重建真实世界材质的同时，通过重参数化强制满足赫姆霍兹互易性，并通过高效解析积分实现能量守恒。系统性分析表明，遵循这些物理定律显著提升了重建材质的视觉质量。此外，通过在RGB通道范数上施加色度约束，进一步提高了神经BRDF的色彩准确性。在多个实测BRDF数据库上的定性和定量实验表明，遵守物理约束能使神经场更忠实、稳定地还原原始数据，并获得更高渲染质量。
原文摘要 · Abstract (English)
We introduce the physically based neural bidirectional reflectance distribution function (PBNBRDF), a novel, continuous representation for material appearance based on neural fields. Our model accurately reconstructs real-world materials while uniquely enforcing physical properties for realistic BRDFs, specifically Helmholtz reciprocity via reparametrization and energy passivity via efficient analytical integration. We conduct a systematic analysis demonstrating the benefits of adhering to these physical laws on the visual quality of reconstructed materials. Additionally, we enhance the color accuracy of neural BRDFs by introducing chromaticity enforcement supervising the norms of RGB channels. Through both qualitative and quantitative experiments on multiple databases of measured real-world BRDFs, we show that adhering to these physical constraints enables neural fields to more faithfully and stably represent the original data and achieve higher rendering quality.
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