ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation
For researchers developing autonomous bronchoscopy systems, ROOM addresses the lack of realistic training data by providing a scalable simulation platform that generates photorealistic sensor data from patient-specific anatomies.
ROOM is a physics-based simulation framework that generates photorealistic bronchoscopy training data from patient CT scans, producing multi-modal sensor data. It validates the data on pose and depth estimation tasks, showing that fine-tuning with ROOM data improves model performance for medical applications.
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.