We consider the problem of reconstructing a full 360° photographic model
of an object from a single image of it. We do so by fitting a neural radiance
field to the image, but find this problem to be severely ill-posed. We thus
take an off-the-self conditional image generator based on diffusion and
engineer a prompt that encourages it to "dream up" novel views of the object.
Using an approach inspired by DreamFields and DreamFusion, we fuse the given
input view, the conditional prior, and other regularizers in a final,
consistent reconstruction. We demonstrate state-of-the-art reconstruction
results on benchmark images when compared to prior methods for monocular 3D
reconstruction of objects. Qualitatively, our reconstructions provide a
faithful match of the input view and a plausible extrapolation of its
appearance and 3D shape, including to the side of the object not visible in the
image.

本文提出了一种基于神经辐射场的方法，通过使用已有的条件图像生成器引导其 “创造” 目标物体的新视图，通过 DreamFields 和 DreamFusion 的启发，将给定的输入视图、条件先验和其他正则化参数融合在一起，从而解决了从单张图像进行 360° 拍摄的问题，并在单眼 3D 建模重建中达到了最先进的效果。