零样本将静态3D场景转为可交互物理场景，自动识别可动部件与关节类型
REACT3D: Recovering Articulations for Interactive Physical 3D Scenes
- 从静态3D场景中自动检测并分割可开合物体
- 推断关节类型与运动参数，实现动作模拟
- 适配主流仿真平台，支持大规模研究应用
交互式3D场景在具身智能中日益重要，但现有数据集受限于标注部分分割、运动类型和轨迹的高成本。我们提出REACT3D，一个可扩展的零样本框架，能将静态3D场景转化为具备一致几何结构的可交互仿真副本，直接用于下游任务。贡献包括：(i) 可开合物体检测与分割，提取候选可动部件；(ii) 关节类型与运动参数推断；(iii) 隐式几何补全后进行可交互物体组装；(iv) 以通用格式集成交互场景，确保与标准仿真平台兼容。在多样室内场景上，检测/分割与关节估计指标均达当前最优，验证了框架有效性，为可动场景理解的大规模研究提供了实用基础。项目主页：https://react3d.github.io/
原文摘要 · Abstract (English)
Interactive 3D scenes are increasingly vital for embodied intelligence, yet existing datasets remain limited due to the labor-intensive process of annotating part segmentation, kinematic types, and motion trajectories. We present REACT3D, a scalable zero-shot framework that converts static 3D scenes into simulation-ready interactive replicas with consistent geometry, enabling direct use in diverse downstream tasks. Our contributions include: (i) openable-object detection and segmentation to extract candidate movable parts from static scenes, (ii) articulation estimation that infers joint types and motion parameters, (iii) hidden-geometry completion followed by interactive object assembly, and (iv) interactive scene integration in widely supported formats to ensure compatibility with standard simulation platforms. We achieve state-of-the-art performance on detection/segmentation and articulation metrics across diverse indoor scenes, demonstrating the effectiveness of our framework and providing a practical foundation for scalable interactive scene generation, thereby lowering the barrier to large-scale research on articulated scene understanding. Our project page is https://react3d.github.io/
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