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：从二维图像实现照片级真实感的物体外观建模一直是视觉与图形学领域的永恒课题。虽然神经隐式方法（如神经辐射场）已展现出高保真度的视角合成能力，但无法对捕捉到的物体进行重光照。近期神经逆渲染方法虽实现了物体重光照，但其将表面属性表示为简单BRDF，因此无法处理半透明物体。我们提出物体中心神经散射函数（OSFs），从图像中学习重建物体外观。OSFs不仅支持自由视角物体重光照，还能同时对不透明与半透明物体进行建模。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，OSFs通过学习从远距离光源到任意空间位置出射方向的辐射传输近似，避免了显式建模复杂的次表面散射，使神经隐式模型的学习成为可行。在真实与合成数据上的实验表明，OSFs能准确重建不透明与半透明物体的外观，从而实现高保真的自由视角重光照与场景合成。