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°摄影模型的问题。通过将神经辐射场拟合到输入图像，我们发现该问题存在严重的不适定性。为此，我们采用基于扩散机制的现成条件图像生成器，设计特定提示以促使其"想象"出物体的新颖视角。受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的一致重建中。在基准图像上的实验表明，与现有单目三维物体重建方法相比，本方法取得了最先进的重建效果。定性评估显示，我们的重建结果既能忠实匹配输入视角，又能合理外推物体的外观与三维形态，包括图像中不可见的物体侧面。