Photorealistic object appearance modeling from 2D images is a constant topic in vision and graphics. While neural implicit methods (such as Neural Radiance Fields) have shown high-fidelity view synthesis results, they cannot relight the captured objects. More recent neural inverse rendering approaches have enabled object relighting, but they represent surface properties as simple BRDFs, and therefore cannot handle translucent objects. We propose Object-Centric Neural Scattering Functions (OSFs) for learning to reconstruct object appearance from only images. OSFs not only support free-viewpoint object relighting, but also can model both opaque and translucent objects. While accurately modeling subsurface light transport for translucent objects can be highly complex and even intractable for neural methods, OSFs learn to approximate the radiance transfer from a distant light to an outgoing direction at any spatial location. This approximation avoids explicitly modeling complex subsurface scattering, making learning a neural implicit model tractable. Experiments on real and synthetic data show that OSFs accurately reconstruct appearances for both opaque and translucent objects, allowing faithful free-viewpoint relighting as well as scene composition.

翻译：从二维图像进行逼真物体外观建模是视觉与图形学领域的经典课题。尽管神经隐式方法（如神经辐射场）已实现高保真视角合成，但无法对捕获物体进行重照明。近期神经逆渲染方法虽可实现物体重照明，但其表面属性仅用简单双向反射分布函数表示，故无法处理半透明物体。本文提出物体中心神经散射函数，通过学习从图像重建物体外观。该函数不仅支持自由视点物体重照明，还能同时建模不透明与半透明物体。尽管精确建模半透明物体的次表面光传输对神经方法而言极其复杂甚至难以处理，OSF通过学习近似从远距离光源到任意空间位置出射方向的辐射度传输，从而避免显式建模复杂的次表面散射，使神经隐式模型的学习变得可行。在真实与合成数据上的实验表明，OSF能准确重建不透明与半透明物体的外观，实现高保真自由视点重照明及场景合成。