Physically Based Neural Bidirectional Reflectance Distribution Function

TL;DR

This paper presents PBNBRDF, a neural model for material appearance that enforces physical laws for realistic and accurate BRDF reconstruction, improving visual quality and stability.

Contribution

The introduction of PBNBRDF, a physically constrained neural BRDF model that enforces Helmholtz reciprocity, energy passivity, and chromaticity for improved realism and fidelity.

Findings

Enhanced visual quality of reconstructed materials.

Higher accuracy and stability in neural BRDF representations.

Effective adherence to physical laws improves rendering results.

Abstract

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.