ROOM：基于物理的连续体机器人仿真器，用于生成逼真的医疗数据集

ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation

September 16, 2025

作者: Salvatore Esposito, Matías Mattamala, Daniel Rebain, Francis Xiatian Zhang, Kevin Dhaliwal, Mohsen Khadem, Subramanian Ramamoorthy

cs.AI

摘要

连续体机器人正在革新支气管镜检查技术，能够深入复杂的肺部气道并实现精准干预。然而，其发展受限于缺乏逼真的训练与测试环境：由于伦理限制和患者安全考虑，真实数据难以获取，而开发自主算法又需要真实的影像和物理反馈。我们推出了ROOM（医学真实光学观测）——一个全面的仿真框架，专为生成逼真的支气管镜训练数据而设计。通过利用患者的CT扫描，我们的流程渲染出多模态传感器数据，包括带有真实噪声和光反射的RGB图像、度量深度图、表面法线、光流以及医学相关尺度的点云。我们在医疗机器人领域的两个经典任务——多视角姿态估计和单目深度估计中验证了ROOM生成的数据，展示了最先进方法在迁移至这些医疗场景时需克服的多样化挑战。此外，我们证明ROOM产生的数据可用于微调现有深度估计模型以应对这些挑战，同时也支持导航等其他下游应用。我们预期ROOM将促进跨多样患者解剖结构和程序场景的大规模数据生成，这些在临床环境中难以捕捉。代码与数据请访问：https://github.com/iamsalvatore/room。

English

Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their development is limited by the lack of realistic training and test environments: Real data is difficult to collect due to ethical constraints and patient safety concerns, and developing autonomy algorithms requires realistic imaging and physical feedback. We present ROOM (Realistic Optical Observation in Medicine), a comprehensive simulation framework designed for generating photorealistic bronchoscopy training data. By leveraging patient CT scans, our pipeline renders multi-modal sensor data including RGB images with realistic noise and light specularities, metric depth maps, surface normals, optical flow and point clouds at medically relevant scales. We validate the data generated by ROOM in two canonical tasks for medical robotics -- multi-view pose estimation and monocular depth estimation, demonstrating diverse challenges that state-of-the-art methods must overcome to transfer to these medical settings. Furthermore, we show that the data produced by ROOM can be used to fine-tune existing depth estimation models to overcome these challenges, also enabling other downstream applications such as navigation. We expect that ROOM will enable large-scale data generation across diverse patient anatomies and procedural scenarios that are challenging to capture in clinical settings. Code and data: https://github.com/iamsalvatore/room.