We consider the problem of reconstructing a full 360{\deg} 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.

翻译：我们研究从单张图像重建物体完整360°摄影测量模型的问题。通过将神经辐射场拟合至输入图像，发现该问题具有严重的不适定性。为此，我们采用基于扩散机制的现成条件图像生成器，并设计提示词引导其"构想"物体的新颖视角。借鉴DreamFields和DreamFusion方法的启发，我们将给定输入视角、条件先验及其他正则化项融合至最终一致性重建中。在基准图像测试中，相较于现有单目三维物体重建方法，本方法展现了最先进的性能。定性分析表明，我们的重建结果既忠实匹配输入视角，又对物体外观与三维形态（包括图像不可见的侧面）实现了合理外推。