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：我们提出了神经微面场（Neural Microfacet Fields），这是一种从场景图像中恢复材质、几何形状和环境光照的方法。该方法在体渲染框架中采用微面反射模型，将每条光线上的每个采样点视为（可能非不透明的）表面。通过在体渲染环境中使用基于表面的蒙特卡洛渲染，我们的方法将表面光传输领域数十年的研究成果与近期用于视图合成的体渲染进展相结合，实现了高效的逆渲染。与现有逆渲染方法相比，我们的方法在捕捉高保真几何形状和高频光照细节方面表现更优；其新视图合成结果与不恢复光照或材质的先进方法相媲美。