  With the widespread usage of VR devices and contents, demands for 3D scene
generation techniques become more popular. Existing 3D scene generation models,
however, limit the target scene to specific domain, primarily due to their
training strategies using 3D scan dataset that is far from the real-world. To
address such limitation, we propose LucidDreamer, a domain-free scene
generation pipeline by fully leveraging the power of existing large-scale
diffusion-based generative model. Our LucidDreamer has two alternate steps:
Dreaming and Alignment. First, to generate multi-view consistent images from
inputs, we set the point cloud as a geometrical guideline for each image
generation. Specifically, we project a portion of point cloud to the desired
view and provide the projection as a guidance for inpainting using the
generative model. The inpainted images are lifted to 3D space with estimated
depth maps, composing a new points. Second, to aggregate the new points into
the 3D scene, we propose an aligning algorithm which harmoniously integrates
the portions of newly generated 3D scenes. The finally obtained 3D scene serves
as initial points for optimizing Gaussian splats. LucidDreamer produces
Gaussian splats that are highly-detailed compared to the previous 3D scene
generation methods, with no constraint on domain of the target scene. Project
page: https://luciddreamer-cvlab.github.io/
