CHOrD：生成具有可控平面图和最优布局的无碰撞、房屋尺度且有序的3D室内场景数字孪生
CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts
AI总结：
本文提出CHOrD框架，利用基于2D图像的中间布局表示和多模态平面图控制，生成房屋尺度、无碰撞、层次化且空间连贯的3D室内数字孪生，并在3D-FRONT等数据集上达到SOTA。
摘要：
我们提出了CHOrD，这是一种用于可扩展合成3D室内场景的新颖框架，旨在创建房屋尺度、无碰撞且具有层次结构的室内数字孪生。与现有方法直接将场景布局合成为场景图或对象列表不同，CHOrD采用了一种基于2D图像的中间布局表示，通过在生成过程中成功将碰撞伪影捕获为分布外（OOD）场景，从而有效防止碰撞伪影。此外，与现有方法不同，CHOrD能够通过多模态控制生成遵循复杂平面图的场景布局，从而创建连贯的、整个房屋范围的布局，对房间结构中的几何和语义变化均具有鲁棒性。另外，我们提出了一个新颖数据集，其覆盖范围扩展到更多家居物品和房间配置，并且数据质量显著提高。CHOrD在3D-FRONT和我们提出的数据集上均展示了最先进的性能，可提供照片级真实感、空间连贯且能适应任意平面图变化的室内场景合成。
英文摘要：
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.
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。
