  We address the estimation of the 6D pose of an unknown target spacecraft
relative to a monocular camera, a key step towards the autonomous rendezvous
and proximity operations required by future Active Debris Removal missions. We
present a novel method that enables an "off-the-shelf" spacecraft pose
estimator, which is supposed to known the target CAD model, to be applied on an
unknown target. Our method relies on an in-the wild NeRF, i.e., a Neural
Radiance Field that employs learnable appearance embeddings to represent
varying illumination conditions found in natural scenes. We train the NeRF
model using a sparse collection of images that depict the target, and in turn
generate a large dataset that is diverse both in terms of viewpoint and
illumination. This dataset is then used to train the pose estimation network.
We validate our method on the Hardware-In-the-Loop images of SPEED+ that
emulate lighting conditions close to those encountered on orbit. We demonstrate
that our method successfully enables the training of an off-the-shelf
spacecraft pose estimation network from a sparse set of images. Furthermore, we
show that a network trained using our method performs similarly to a model
trained on synthetic images generated using the CAD model of the target.
