  We consider the problem of tracking the 6D pose of a moving RGB-D camera in a
neural scene representation. Different such representations have recently
emerged, and we investigate the suitability of them for the task of camera
tracking. In particular, we propose to track an RGB-D camera using a signed
distance field-based representation and show that compared to density-based
representations, tracking can be sped up, which enables more robust and
accurate pose estimates when computation time is limited.
