ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic
  Medical Datasets Generation
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.
68caaee95a7803ff3be42bae
Salvatore Esposito
claimed_verified
2025-09-17T12:55:36.197Z
67a659fad1284b551f110ce2
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Salvatore Esposito
user
iamsalvatore
68caaee95a7803ff3be42baf
Matías Mattamala
68caaee95a7803ff3be42bb0
Daniel Rebain
68caaee95a7803ff3be42bb1
Francis Xiatian Zhang
68caaee95a7803ff3be42bb2
Kevin Dhaliwal
68caaee95a7803ff3be42bb3
Mohsen Khadem
68caaee95a7803ff3be42bb4
Subramanian Ramamoorthy
ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic
  Medical Datasets Generation
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.
ROOM is a simulation framework that generates photorealistic bronchoscopy training data using patient CT scans, enabling the development and validation of autonomy algorithms in medical robotics.
photorealistic bronchoscopy training data multi-modal sensor data RGB images realistic noise light specularities metric depth maps surface normals optical flow point clouds multi-view pose estimation monocular depth estimation depth estimation models navigation