  State-of-the-art research of traditional computer vision is increasingly
leveraged in the surgical domain. A particular focus in computer-assisted
surgery is to replace marker-based tracking systems for instrument localization
with pure image-based 6DoF pose estimation using deep-learning methods.
However, state-of-the-art single-view pose estimation methods do not yet meet
the accuracy required for surgical navigation. In this context, we investigate
the benefits of multi-view setups for highly accurate and occlusion-robust 6DoF
pose estimation of surgical instruments and derive recommendations for an ideal
camera system that addresses the challenges in the operating room.
  The contributions of this work are threefold. First, we present a
multi-camera capture setup consisting of static and head-mounted cameras, which
allows us to study the performance of pose estimation methods under various
camera configurations. Second, we publish a multi-view RGB-D video dataset of
ex-vivo spine surgeries, captured in a surgical wet lab and a real operating
theatre and including rich annotations for surgeon, instrument, and patient
anatomy. Third, we evaluate three state-of-the-art single-view and multi-view
methods for the task of 6DoF pose estimation of surgical instruments and
analyze the influence of camera configurations, training data, and occlusions
on the pose accuracy and generalization ability. The best method utilizes five
cameras in a multi-view pose optimization and achieves an average position and
orientation error of 1.01 mm and 0.89\deg for a surgical drill as well as 2.79
mm and 3.33\deg for a screwdriver under optimal conditions. Our results
demonstrate that marker-less tracking of surgical instruments is becoming a
feasible alternative to existing marker-based systems.
