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Purpose: We describe a 3D multi-view perception system for the da Vincisurgical system to enable Operating room (OR) scene understanding and contextawareness.  Methods: Our proposed system is comprised of four Time-of-Flight (ToF)cameras rigidly attached to strategic locations on the daVinci Xi patient sidecart (PSC). The cameras are registered to the robot's kinematic chain byperforming a one-time calibration routine and therefore, information from allcameras can be fused and represented in one common coordinate frame. Based onthis architecture, a multi-view 3D scene semantic segmentation algorithm iscreated to enable recognition of common and salient objects/equipment andsurgical activities in a da Vinci OR. Our proposed 3D semantic segmentationmethod has been trained and validated on a novel densely annotated dataset thathas been captured from clinical scenarios.  Results: The results show that our proposed architecture has acceptableregistration error ($3.3\%\pm1.4\%$ of object-camera distance) and can robustlyimprove scene segmentation performance (mean Intersection Over Union - mIOU)for less frequently appearing classes ($\ge 0.013$) compared to a single-viewmethod.  Conclusion: We present the first dynamic multi-view perception system with anovel segmentation architecture, which can be used as a building blocktechnology for applications such as surgical workflow analysis, automation ofsurgical sub-tasks and advanced guidance systems.

A Robotic 3D Perception System for Operating Room Environment Awareness