CHOrD：生成无碰撞、房屋规模且有序的3D室内场景数字孪生，具备可控平面图与最优布局

CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts

March 15, 2025

作者: Chong Su, Yingbin Fu, Zheyuan Hu, Jing Yang, Param Hanji, Shaojun Wang, Xuan Zhao, Cengiz Öztireli, Fangcheng Zhong

cs.AI

摘要

我们推出CHOrD，一个用于可扩展三维室内场景合成的新颖框架，旨在创建房屋规模、无碰撞且层次结构化的室内数字孪生体。与现有方法直接以场景图或对象列表形式合成场景布局不同，CHOrD引入了一种基于二维图像的中间布局表示，通过在生成过程中成功捕捉分布外（OOD）场景，有效预防了碰撞伪影。此外，区别于现有技术，CHOrD能够生成遵循复杂平面图的多模态控制场景布局，从而创造出在房间结构的几何与语义变化上均保持一致的整屋布局。同时，我们提出了一套新数据集，该数据集在家庭物品和房间配置的覆盖范围上有所扩展，数据质量也显著提升。CHOrD在3D-FRONT及我们提出的数据集上均展现了最先进的性能，实现了适应任意平面图变化、空间连贯且逼真的室内场景合成。

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.