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FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling.
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FiG-NeRF FiG-NeRF: Figure Ground Neural Radiance Fields for 3D Object Category Modelling Christopher Xie 1 , Keunhong Park 1 , Ricardo Martin-Brualla 2 , Matthew Brown 2 1 University of Washington, 2 Google Research International Conference on 3D Vision - 3DV, 2021 arXiv FiG-NeRF can learn high quality 3D object category models from casually captured images of objects. Abstract 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 onl...
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