We present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the object and its use as a guide for the reconstruction of the underlying radiance field. Our formulation is based on a generative process that first maps a latent code to a voxelized shape, and then renders it to an image, with the object appearance being controlled by a second latent code. During inference, we optimize both the latent codes and the networks to fit a test image of a new object. The explicit disentanglement of shape and appearance allows our model to be fine-tuned given a single image. We can then render new views in a geometrically consistent manner and they represent faithfully the input object. Additionally, our method is able to generalize to images outside of the training domain (more realistic renderings and even real photographs). Finally, the inferred geometric scaffold is itself an accurate estimate of the object's 3D shape. We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.

中文翻译：

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ShaRF：单视图的形状条件辐射场

我们提出了一种仅给出单个图像即可估计对象的神经场景表示的方法。我们方法的核心是估算对象的几何支架，并将其用作基础辐射场重建的指导。我们的公式基于生成过程，该过程首先将潜在代码映射到体素化的形状，然后将其渲染为图像，而对象外观由第二个潜在代码控制。在推论过程中，我们同时优化了潜在代码和网络，以适应新对象的测试图像。形状和外观的明显分离使我们的模型在给定单个图像时可以进行微调。然后，我们可以以几何上一致的方式渲染新视图，它们忠实地表示输入对象。此外，我们的方法能够推广到训练领域之外的图像（更真实的渲染，甚至是真实的照片）。最后，推断出的几何支架本身就是对对象3D形状的准确估计。我们在几个实验中证明了我们的方法在合成图像和真实图像中的有效性。