  In advanced mission concepts with high levels of autonomy, spacecraft need to
internally model the pose and shape of nearby orbiting objects. Recent works in
neural scene representations show promising results for inferring generic
three-dimensional scenes from optical images. Neural Radiance Fields (NeRF)
have shown success in rendering highly specular surfaces using a large number
of images and their pose. More recently, Generative Radiance Fields (GRAF)
achieved full volumetric reconstruction of a scene from unposed images only,
thanks to the use of an adversarial framework to train a NeRF. In this paper,
we compare and evaluate the potential of NeRF and GRAF to render novel views
and extract the 3D shape of two different spacecraft, the Soil Moisture and
Ocean Salinity satellite of ESA's Living Planet Programme and a generic cube
sat. Considering the best performances of both models, we observe that NeRF has
the ability to render more accurate images regarding the material specularity
of the spacecraft and its pose. For its part, GRAF generates precise novel
views with accurate details even when parts of the satellites are shadowed
while having the significant advantage of not needing any information about the
relative pose.
