  The operating room (OR) is an environment of interest for the development of
sensing systems, enabling the detection of people, objects, and their semantic
relations. Due to frequent occlusions in the OR, these systems often rely on
input from multiple cameras. While increasing the number of cameras generally
increases algorithm performance, there are hard limitations to the number and
locations of cameras in the OR. Neural Radiance Fields (NeRF) can be used to
render synthetic views from arbitrary camera positions, virtually enlarging the
number of cameras in the dataset. In this work, we explore the use of NeRF for
view synthesis of dynamic scenes in the OR, and we show that regularisation
with depth supervision from RGB-D sensor data results in higher image quality.
We optimise a dynamic depth-supervised NeRF with up to six synchronised cameras
that capture the surgical field in five distinct phases before and during a
knee replacement surgery. We qualitatively inspect views rendered by a virtual
camera that moves 180 degrees around the surgical field at differing time
values. Quantitatively, we evaluate view synthesis from an unseen camera
position in terms of PSNR, SSIM and LPIPS for the colour channels and in MAE
and error percentage for the estimated depth. We find that NeRFs can be used to
generate geometrically consistent views, also from interpolated camera
positions and at interpolated time intervals. Views are generated from an
unseen camera pose with an average PSNR of 18.2 and a depth estimation error of
2.0%. Our results show the potential of a dynamic NeRF for view synthesis in
the OR and stress the relevance of depth supervision in a clinical setting.
