CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts
Abstract
The utilization of monocular vision for non-cooperative spacecraft pose estimation has been significantly researched in space target monitoring, on-orbit servicing, and satellite maintenance. The challenge lies in addressing the cross-domain variations in shape, texture, lighting, and motion patterns between simulated and real captured images. To tackle this issue, a novel domain adaptation 6DoF pose estimation algorithm is proposed to extract the geometric and semantic consistency between cross-domain training and testing datasets. Experimental results demonstrate that our pose estimation method achieves state-of-the-art performance on the SPARK2024 dataset.
Cite
Text
Zuo et al. "CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00679Markdown
[Zuo et al. "CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/zuo2024cvprw-crospace6d/) doi:10.1109/CVPRW63382.2024.00679BibTeX
@inproceedings{zuo2024cvprw-crospace6d,
title = {{CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts}},
author = {Zuo, Jianhong and Zhang, Shengyang and Zhang, Qianyu and Zhao, Yutao and Liu, Baichuan and Wu, Aodi and Wan, Xue and Shu, Leizheng and Kang, Guohua},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {6857-6863},
doi = {10.1109/CVPRW63382.2024.00679},
url = {https://mlanthology.org/cvprw/2024/zuo2024cvprw-crospace6d/}
}