用物理模型解耦光照与材质，实现任意光照下的逼真渲染。
Beyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering
- 基于物理的神经延迟着色，分离光照与材质参数
- 在任意光照下生成逼真图像，优于现有神经着色方法
- 适合影视特效与游戏场景的可调光照渲染
基于深度学习的渲染在逼真图像合成方面取得显著进展，可用于电影视觉效果和游戏场景构建。然而，其主要局限在于难以分解光照与材质参数，导致仅能重建输入场景而无法控制这些变量。本文提出一种基于物理的神经延迟着色新流程，将数据驱动渲染过程解耦，学习可泛化的着色函数，以实现逼真着色与再照明任务；同时提出阴影估计器，高效模拟阴影效果。相比经典模型与当前最优神经着色方法，本模型性能更优，支持从任意光照输入生成通用且逼真的渲染结果。
原文摘要 · Abstract (English)
Deep learning based rendering has achieved major improvements in photo-realistic image synthesis, with potential applications including visual effects in movies and photo-realistic scene building in video games. However, a significant limitation is the difficulty of decomposing the illumination and material parameters, which limits such methods to reconstructing an input scene, without any possibility to control these parameters. This paper introduces a novel physics based neural deferred shading pipeline to decompose the data-driven rendering process, learn a generalizable shading function to produce photo-realistic results for shading and relighting tasks; we also propose a shadow estimator to efficiently mimic shadowing effects. Our model achieves improved performance compared to classical models and a state-of-art neural shading model, and enables generalizable photo-realistic shading from arbitrary illumination input.
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