生成无碰撞、可控布局的3D家居数字孪生，支持任意户型
CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts
- 用2D图像中间表示避免物体穿模，将穿模识别为异常场景
- 支持复杂户型与多模态控制，生成全局一致的房间布局
- 适合需要高精度3D场景生成的建筑、家居设计应用
我们提出CHOrD，一种可扩展的3D室内场景合成框架，用于创建房屋尺度、无碰撞且分层结构化的室内数字孪生。与直接生成场景图或物体列表的方法不同，CHOrD引入基于2D图像的中间布局表示，通过将碰撞问题识别为分布外（OOD）情形，在生成阶段有效避免穿模。此外，该方法能生成符合复杂户型的多模态可控布局，对房间几何和语义变化具有鲁棒性。我们还构建了一个新数据集，涵盖更丰富的家居物品与房间配置，且数据质量显著提升。CHOrD在3D-FRONT及自建数据集上均达到当前最佳性能，实现可适应任意户型变化的逼真、空间连贯的3D室内场景合成。
原文摘要 · Abstract (English)
We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically structured indoor digital twins. In contrast to existing methods that directly synthesize the scene layout as a scene graph or object list, CHOrD incorporates a 2D image-based intermediate layout representation, enabling effective prevention of collision artifacts by successfully capturing them as out-of-distribution (OOD) scenarios during generation. Furthermore, unlike existing methods, CHOrD is capable of generating scene layouts that adhere to complex floor plans with multi-modal controls, enabling the creation of coherent, house-wide layouts robust to both geometric and semantic variations in room structures. Additionally, we propose a novel dataset with expanded coverage of household items and room configurations, as well as significantly improved data quality. CHOrD demonstrates state-of-the-art performance on both the 3D-FRONT and our proposed datasets, delivering photorealistic, spatially coherent indoor scene synthesis adaptable to arbitrary floor plan variations.
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