  Neural fields provide a continuous scene representation of 3D geometry and
appearance in a way which has great promise for robotics applications. One
functionality that unlocks unique use-cases for neural fields in robotics is
object 6-DoF registration. In this paper, we provide an expanded analysis of
the recent Reg-NF neural field registration method and its use-cases within a
robotics context. We showcase the scenario of determining the 6-DoF pose of
known objects within a scene using scene and object neural field models. We
show how this may be used to better represent objects within imperfectly
modelled scenes and generate new scenes by substituting object neural field
models into the scene.
