We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene. Our method uses a microfacet reflectance model within a volumetric setting by treating each sample along the ray as a (potentially non-opaque) surface. Using surface-based Monte Carlo rendering in a volumetric setting enables our method to perform inverse rendering efficiently by combining decades of research in surface-based light transport with recent advances in volume rendering for view synthesis. Our approach outperforms prior work in inverse rendering, capturing high fidelity geometry and high frequency illumination details; its novel view synthesis results are on par with state-of-the-art methods that do not recover illumination or materials.

翻译：我们提出神经微面元场（Neural Microfacet Fields），这是一种从场景图像中恢复材质、几何形状及环境光照的方法。本方法通过将光线上的每个采样点视为（可能非不透明的）表面，在体积渲染框架内采用微面元反射模型。在体积渲染中结合基于表面的蒙特卡洛渲染技术，使我们能够将数十年表面光传输研究成果与近年来用于视点合成的体积渲染进展相融合，从而高效实现逆向渲染。我们的方法在逆向渲染方面优于先前工作，可捕获高保真几何结构与高频光照细节；其新颖视点合成结果与不涉及光照或材质恢复的先进方法性能相当。