  Reliable and stable 6D pose estimation of uncooperative space objects plays
an essential role in on-orbit servicing and debris removal missions.
Considering that the pose estimator is sensitive to background interference,
this paper proposes a counterfactual analysis framework named CASpaceNet to
complete robust 6D pose estimation of the spaceborne targets under complicated
background. Specifically, conventional methods are adopted to extract the
features of the whole image in the factual case. In the counterfactual case, a
non-existent image without the target but only the background is imagined. Side
effect caused by background interference is reduced by counterfactual analysis,
which leads to unbiased prediction in final results. In addition, we also carry
out lowbit-width quantization for CA-SpaceNet and deploy part of the framework
to a Processing-In-Memory (PIM) accelerator on FPGA. Qualitative and
quantitative results demonstrate the effectiveness and efficiency of our
proposed method. To our best knowledge, this paper applies causal inference and
network quantization to the 6D pose estimation of space-borne targets for the
first time. The code is available at
https://github.com/Shunli-Wang/CA-SpaceNet.

Computer Vision and Pattern Recognition