On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches
This work provides a systematic analysis for researchers in computer graphics and vision, but it is incremental as it focuses on comparing and extending existing methods rather than introducing a new paradigm.
The paper tackles the lack of comprehensive comparison among neural BRDF modeling approaches by evaluating several state-of-the-art methods, proposing two extensions: an additive combination strategy for splitting reflectance and an input mapping to ensure exact reciprocity.
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.