对比主流神经BRDF建模方法，提出可精确保证对称性的新策略。
On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches
- 将反射分为漫反射与镜面部分，用加法组合提升建模精度
- 提出新输入映射使双向反射满足精确对称性，优于以往软约束方法
- 首次系统评估多种神经BRDF方法在保真度与物理一致性上的表现
双向反射分布函数（BRDF）是描述光与物质复杂交互的核心工具。近期多项研究采用神经方法进行BRDF建模，策略各异，从基于现有参数模型到纯神经参数化。尽管各方法均取得显著效果，但缺乏全面比较。本文对多种方法进行了系统评估，涵盖定性和定量重建质量，并分析了对称性与能量守恒特性。此外，提出两个可集成至现有方法的改进：一种新的神经BRDF加法组合策略，将反射分解为漫反射与镜面成分；以及一种输入映射机制，通过构造确保精确对称性，而此前方法仅依赖软约束实现。
原文摘要 · Abstract (English)
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.
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