CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts

Jianhong Zuo, Shengyang Zhang, Qianyu Zhang, Yutao Zhao, Baichuan Liu, Aodi Wu, Xue Wan, Leizheng Shu, Guohua Kang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6857-6863

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.

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[bibtex]
@InProceedings{Zuo_2024_CVPR, 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}, title = {CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {6857-6863} }