  Text-to-3D generation has achieved significant success by incorporating
powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to
the inconsistency of 3D geometry. Recently, since large-scale multi-view
datasets have been released, fine-tuning the diffusion model on the multi-view
datasets becomes a mainstream to solve the 3D inconsistency problem. However,
it has confronted with fundamental difficulties regarding the limited quality
and diversity of 3D data, compared with 2D data. To sidestep these trade-offs,
we explore a retrieval-augmented approach tailored for score distillation,
dubbed ReDream. We postulate that both expressiveness of 2D diffusion models
and geometric consistency of 3D assets can be fully leveraged by employing the
semantically relevant assets directly within the optimization process. To this
end, we introduce novel framework for retrieval-based quality enhancement in
text-to-3D generation. We leverage the retrieved asset to incorporate its
geometric prior in the variational objective and adapt the diffusion model's 2D
prior toward view consistency, achieving drastic improvements in both geometry
and fidelity of generated scenes. We conduct extensive experiments to
demonstrate that ReDream exhibits superior quality with increased geometric
consistency. Project page is available at https://ku-cvlab.github.io/ReDream/.
