We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able to do this whilst simultaneously separating foreground objects from their varying backgrounds. We achieve this via a 2-component NeRF model, FiG-NeRF, that prefers explanation of the scene as a geometrically constant background and a deformable foreground that represents the object category. We show that this method can learn accurate 3D object category models using only photometric supervision and casually captured images of the objects. Additionally, our 2-part decomposition allows the model to perform accurate and crisp amodal segmentation. We quantitatively evaluate our method with view synthesis and image fidelity metrics, using synthetic, lab-captured, and in-the-wild data. Our results demonstrate convincing 3D object category modelling that exceed the performance of existing methods.

本研究使用神经辐射场（NeRF）从输入图像集合中学习高质量的三维物体类别模型，通过2组件NeRF模型，FiG-NeRF，实现场景的几何恒定背景和可变形前景的分离，从而仅使用光度监督和随意捕捉的物体图像即可学习准确的三维物体类别模型，并且可以进行精确清晰的无模分割和视图合成，并使用综合测量方法对方法进行定量评估。

FiG-NeRF：面向三维对象分类建模的图形-背景神经辐射场