The techniques for 3D indoor scene capturing are widely used, but the meshes
produced leave much to be desired. In this paper, we propose "RoomDreamer",
which leverages powerful natural language to synthesize a new room with a
different style. Unlike existing image synthesis methods, our work addresses
the challenge of synthesizing both geometry and texture aligned to the input
scene structure and prompt simultaneously. The key insight is that a scene
should be treated as a whole, taking into account both scene texture and
geometry. The proposed framework consists of two significant components:
Geometry Guided Diffusion and Mesh Optimization. Geometry Guided Diffusion for
3D Scene guarantees the consistency of the scene style by applying the 2D prior
to the entire scene simultaneously. Mesh Optimization improves the geometry and
texture jointly and eliminates the artifacts in the scanned scene. To validate
the proposed method, real indoor scenes scanned with smartphones are used for
extensive experiments, through which the effectiveness of our method is
demonstrated.

本文提出了基于自然语言的房间风格合成算法 RoomDreamer，通过 Geometry Guided Diffusion 和 Mesh Optimization 处理实现 3D 室内场景生成，大幅提升了几何和纹理的一致性和图像质量。