发表机构
Ubisoft La Forge; Inria（育碧实验室; 法国国家信息与自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合GGX型微表面模型与神经模型的混合BRDF模型，以较小网络实现更优近似效果，兼具易用性，适用于离线与实时渲染。
AI 中文摘要
在过去十年中，基于微表面的BRDF（双向反射分布函数）模型已成为实时渲染管线的基础。尽管这些模型被广泛使用，但它们往往无法复现复杂光-表面相互作用产生的细微外观效果，这导致了针对特定光学现象（如衍射、虹彩、多层结构）的专用基于物理的模型的出现。这些模型虽然更准确，但通用性较差，且实时渲染性能不足。最近推出的神经模型已展示出近似来自测量、模拟甚至复杂着色网络的BRDF参考数据的能力。然而，大多数当前的神经模型需要相对较大的网络，这使得它们在实时渲染中成本较高。在本文中，我们引入了一种混合模型，它结合了GGX型微表面模型和神经模型，以利用两种表示的最佳特性。神经组件修正微表面组件近似的外观，从而允许使用比现有神经模型小得多的网络。我们表明，在相同的内存成本下，我们的模型比最先进的神经模型更好地近似测量结果，同时与基于微表面的模型相比评估开销更低。此外，我们的混合模型仍然易于艺术家编辑，并且受益于重要的采样方案，使其对离线和实时渲染都具有吸引力。
英文摘要
Over the past decade, microfacet-based BRDF models have formed the foundation of real-time rendering pipelines. Despite their widespread use, they often fail to reproduce subtle appearance effects arising from complex light-surface interactions, which have led to the emergence of specialized physics-based models for specific optical phenomena (e.g., diffraction, iridescence, multilayers). Although more accurate, these models lose versatility and lack performance for real-time rendering. Recently introduced, neural models have demonstrated their ability to approximate BRDF reference data coming from measurements, simulations, or even complex shading networks. However, most current neural models require relatively large networks, making them costly for real-time rendering. In this paper, we introduce a hybrid model that combines a GGX-type microfacet model and a neural model to leverage the best features of both representations. The neural component corrects the appearance approximated by the microfacet component, allowing much smaller network than in existing neural models. We show that, at identical memory cost, our model approximates measurements better than state-of-the-art neural models for a low evaluation overhead compared to a microfacet-based model. Furthermore, our hybrid model remains easily editable by artists and benefits from an important sampling scheme, making it attractive for both offline and real-time rendering.
Comments13 pages, 12 figures, conference, project page see https://ubisoft-laforge.github.io/world/hybridrdf
Journal refEurographics Symposium on Rendering (EGSR) 2026
