Wide-Depth-Range 6D Object Pose Estimation in Space
This addresses pose estimation for space applications where objects are visible from great distances, though it appears incremental as it improves upon prior two-stage methods for scale variation.
The paper tackles 6D object pose estimation in space, where large scale variations due to lack of atmospheric scattering pose challenges, by proposing a single-stage hierarchical end-to-end trainable network that is more robust to scale variations. It demonstrates that this approach outperforms existing methods on both space-like synthesized images and standard benchmarks.
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.