Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e.g., the Janus face problem or the content drift problem, in zero-shot text-to-3D models. However, the 3D geometric fidelity of the output remains an unresolved issue; albeit the rendered 2D views are realistic, the underlying geometry may contain errors such as unreasonable concavities. In this work, we propose CorrespondentDream, an effective method to leverage annotation-free, cross-view correspondences yielded from the diffusion U-Net to provide additional 3D prior to the NeRF optimization process. We find that these correspondences are strongly consistent with human perception, and by adopting it in our loss design, we are able to produce NeRF models with geometries that are more coherent with common sense, e.g., more smoothed object surface, yielding higher 3D fidelity. We demonstrate the efficacy of our approach through various comparative qualitative results and a solid user study.

利用多视角扩散模型作为3D优化的先验条件，缓解了零样本文本到3D模型中的3D一致性问题，如Janus面问题或内容漂移问题。然而，输出的3D几何保真度仍然是一个未解决的问题；尽管渲染的2D视图是逼真的，但其底层几何可能包含不合理的凹面。我们提出了一种有效的方法CorrespondentDream，利用来自扩散U-Net的无注释交叉视图对应关系，为NeRF优化过程提供额外的3D先验。我们发现这些对应关系与人类感知力强烈一致，并通过采用它们在我们的损失设计中，能够生成与常识更一致的NeRF模型几何，例如更平滑的物体表面，从而提高3D保真度。通过各种比较性定性结果和扎实的用户研究，我们证明了我们方法的有效性。

利用跨视图对应关系增强文本到三维的三维保真度