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、几何形状和环境照明的方法。该方法在体渲染框架内采用微面反射模型，将光线上的每个采样点视为（可能非不透明的）表面。通过在体渲染设置中使用基于表面的蒙特卡洛渲染，我们的方法能够将表面光传输领域数十年的研究成果与体渲染在视图合成方面的最新进展相结合，从而高效执行逆渲染。我们的方法在逆渲染方面优于先前的工作，能够捕获高保真几何形状和高频照明细节；其新颖视图合成结果与不恢复照明或材质的先进方法相当。