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.

翻译：本文提出基于物理的神经双向反射分布函数（PBNBRDF），这是一种基于神经场构建的、用于描述材料外观的新型连续表示方法。该模型能精确重建真实世界材料，同时通过独特机制强制满足真实BRDF的物理特性：具体而言，通过重参数化实现亥姆霍兹互易性，并通过高效解析积分保证能量被动性。我们通过系统分析证明了遵循这些物理定律对重建材料视觉质量的提升作用。此外，我们通过引入监督RGB通道范数的色度约束机制，提升了神经BRDF的色彩精度。通过在多个实测真实世界BRDF数据库上进行的定性与定量实验，我们证明遵循这些物理约束能使神经场更忠实、稳定地表征原始数据，并获得更高的渲染质量。