TwinOR：动态手术室的逼真数字双胞胎用于具身AI研究
TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research
AI总结：
TwinOR通过构建高保真的动态手术室数字双胞胎，为具身AI研究提供安全可控的仿真环境，实现厘米级精度的手术室几何重建与动态交互模拟，验证了其在视觉定位和几何理解任务中的真实感。
摘要：
开发智能手术系统所需的具身AI需要安全、可控的环境进行持续学习和评估。然而，手术室（ORs）中的安全规定和操作限制限制了智能体在真实环境中自由感知和交互。数字双胞胎提供高保真、无风险的环境用于探索和训练。如何创建动态的OR数字表示，以捕捉相关空间、视觉和行为复杂性仍然是一个开放挑战。我们介绍了TwinOR，一种实-仿基础设施，用于构建逼真且动态的OR数字双胞胎。该系统重建静态几何并连续建模人类和设备的运动。静态和动态组件融合成一个沉浸式的3D环境，支持可控的仿真并促进未来的具身探索。所提出的框架在厘米级精度下重建完整的OR几何，同时在手术流程中保持动态交互。在我们的实验中，TwinOR合成立体和单目RGB流以及深度观测用于几何理解和视觉定位任务。在TwinOR合成数据上评估的模型如FoundationStereo和ORB-SLAM3在真实世界室内数据集上表现在其报告的准确性范围内，证明了TwinOR提供的传感器级真实感足以模拟真实世界感知和定位挑战。通过建立感知基础的实-仿管道，TwinOR使动态、逼真的OR数字双胞胎的自动构建成为可能。作为安全且可扩展的实验环境，TwinOR为将具身智能从仿真转化为真实世界临床环境开辟了新机会。
英文摘要：
Developing embodied AI for intelligent surgical systems requires safe, controllable environments for continual learning and evaluation. However, safety regulations and operational constraints in operating rooms (ORs) limit agents from freely perceiving and interacting in realistic settings. Digital twins provide high-fidelity, risk-free environments for exploration and training. How we may create dynamic digital representations of ORs that capture relevant spatial, visual, and behavioral complexity remains an open challenge. We introduce TwinOR, a real-to-sim infrastructure for constructing photorealistic and dynamic digital twins of ORs. The system reconstructs static geometry and continuously models human and equipment motion. The static and dynamic components are fused into an immersive 3D environment that supports controllable simulation and facilitates future embodied exploration. The proposed framework reconstructs complete OR geometry with centimeter-level accuracy while preserving dynamic interaction across surgical workflows. In our experiments, TwinOR synthesizes stereo and monocular RGB streams as well as depth observations for geometry understanding and visual localization tasks. Models such as FoundationStereo and ORB-SLAM3 evaluated on TwinOR-synthesized data achieve performance within their reported accuracy ranges on real-world indoor datasets, demonstrating that TwinOR provides sensor-level realism sufficient for emulating real-world perception and localization challenge. By establishing a perception-grounded real-to-sim pipeline, TwinOR enables the automatic construction of dynamic, photorealistic digital twins of ORs. As a safe and scalable environment for experimentation, TwinOR opens new opportunities for translating embodied intelligence from simulation to real-world clinical environments.
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。
