RoomDreamer：具有连贯几何与纹理的文本驱动3D室内场景合成
RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture
AI总结：
提出RoomDreamer框架，通过Geometry Guided Diffusion和Mesh Optimization两个组件，利用自然语言同时合成与输入场景结构和提示对齐的几何与纹理，生成不同风格的3D室内场景。
摘要：
3D室内场景捕获技术被广泛使用，但生成的网格（mesh）仍有诸多不足。在本文中，我们提出了“RoomDreamer”，它利用强大的自然语言来合成具有不同风格的新房间。与现有的图像合成方法不同，我们的工作解决了同时合成与输入场景结构和提示对齐的几何和纹理的挑战。关键见解是应将场景作为整体处理，同时考虑场景纹理和几何。所提出的框架由两个重要组件组成：Geometry Guided Diffusion和Mesh Optimization。用于3D场景的Geometry Guided Diffusion通过将2D先验同时应用于整个场景来保证场景风格的一致性。Mesh Optimization联合改进几何和纹理，并消除扫描场景中的伪影。为了验证所提出的方法，使用智能手机扫描的真实室内场景进行了大量实验，证明了我们方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
