  6D pose estimation in space poses unique challenges that are not commonly
encountered in the terrestrial setting. One of the most striking differences is
the lack of atmospheric scattering, allowing objects to be visible from a great
distance while complicating illumination conditions. Currently available
benchmark datasets do not place a sufficient emphasis on this aspect and mostly
depict the target in close proximity.
  Prior work tackling pose estimation under large scale variations relies on a
two-stage approach to first estimate scale, followed by pose estimation on a
resized image patch. We instead propose a single-stage hierarchical end-to-end
trainable network that is more robust to scale variations. We demonstrate that
it outperforms existing approaches not only on images synthesized to resemble
images taken in space but also on standard benchmarks.
