On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches

Florian Hofherr, Bjoern Haefner, Daniel Cremers; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1785-1794

The bidirectional reflectance distribution function (BRDF) is an essential tool to capture the complex interaction of light and matter. Recently several works have employed neural methods for BRDF modeling following various strategies ranging from utilizing existing parametric models to purely neural parametrizations. While all methods yield impressive results a comprehensive comparison of the different approaches is missing in the literature. In this work we present a thorough evaluation of several approaches including results for qualitative and quantitative reconstruction quality and an analysis of reciprocity and energy conservation. Moreover we propose two extensions that can be added to existing approaches: A novel additive combination strategy for neural BRDFs that split the reflectance into a diffuse and a specular part and an input mapping that ensures reciprocity exactly by construction while previous approaches only ensure it by soft constraints.