  Many approaches have been proposed for human pose estimation in single and
multi-view RGB images. However, some environments, such as the operating room,
are still very challenging for state-of-the-art RGB methods. In this paper, we
propose an approach for multi-view 3D human pose estimation from RGB-D images
and demonstrate the benefits of using the additional depth channel for pose
refinement beyond its use for the generation of improved features. The proposed
method permits the joint detection and estimation of the poses without knowing
a priori the number of persons present in the scene. We evaluate this approach
on a novel multi-view RGB-D dataset acquired during live surgeries and
annotated with ground truth 3D poses.
