Physically Based Neural Bidirectional Reflectance Distribution Function
This work improves material representation for computer graphics applications, though it is incremental by building on existing neural field approaches with specific physical constraints.
The paper tackles the problem of representing material appearance in computer graphics by introducing a physically based neural bidirectional reflectance distribution function (PBNBRDF) that enforces physical properties like Helmholtz reciprocity and energy passivity, achieving higher rendering quality and more faithful reconstruction of real-world materials compared to previous methods.
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.