CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space

TL;DR

This paper introduces CA-SpaceNet, a novel framework that employs counterfactual analysis and quantization to improve the robustness and efficiency of 6D pose estimation for space objects amidst background interference.

Contribution

The paper presents the first application of causal inference and network quantization to 6D pose estimation of space targets, enhancing robustness and computational efficiency.

Findings

Counterfactual analysis reduces background interference effects.

Quantization and FPGA deployment improve processing speed.

Method achieves state-of-the-art accuracy in space object pose estimation.

Abstract

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.