Multi-frequency Feature Interaction and Dynamic Fusion for Spacecraft 6D Pose Estimation

TONG Wei; LIN Xi; YAN Ying; LIN Jinxing; LI Tao; WU Qi

doi:10.11999/JEIT260452

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TONG Wei, LIN Xi, YAN Ying, LIN Jinxing, LI Tao, WU Qi. Multi-frequency Feature Interaction and Dynamic Fusion for Spacecraft 6D Pose Estimation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260452

Citation:

TONG Wei, LIN Xi, YAN Ying, LIN Jinxing, LI Tao, WU Qi. Multi-frequency Feature Interaction and Dynamic Fusion for Spacecraft 6D Pose Estimation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260452

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Multi-frequency Feature Interaction and Dynamic Fusion for Spacecraft 6D Pose Estimation

doi: 10.11999/JEIT260452 cstr: 32379.14.JEIT260452

TONG Wei^{1, 2, 3},
LIN Xi⁴,
YAN Ying^{1, 5
,
,},
LIN Jinxing²,
LI Tao¹,
WU Qi⁶

1.
School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3.
Key Laboratory of Social Computing and Cognitive Intelligence, Dalian 116024, China
4.
School of Computer and Information Technology, Shanxi University, Taiyuan 030031, China
5.
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230009, China
6.
Computer College, Shanghai Jiaotong University, Shanghai 200042, China

Funds: The National Natural Science Foundation of China(62405145), The Natural Science Foundation of Jiangsu Province(BK20240641), The Key Laboratory of Social Computing and Cognitive Intelligence(SCCI2023YB02), Nanjing University of Information Science & Technology, The Quality Assurance and Evaluation of higher Education in Jiangsu Province (2025JSETKT158)

Received Date: 2026-04-15
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-17

Available Online: 2026-08-26

Abstract

Abstract

Spacecraft Six-Dimensional (6D) pose estimation aims to determine the relative pose of a target spacecraft with respect to a servicing spacecraft in a spatial coordinate system. It is a key step in close-range operations, including defunct satellite removal, space debris capture, on-orbit spacecraft manipulation, and space station rendezvous and docking. In recent years, Convolutional Neural Network (CNN)-based 6D pose estimation methods have received considerable attention. However, their strong dependence on convolutional architectures makes them sensitive to image textures and limits their ability to model long-range contextual information effectively. Moreover, mainstream methods typically adopt a pipeline consisting of object detection followed by pose estimation. Such methods provide limited diversity in feature extraction and are sensitive to low-light conditions and complex background interference. To address these problems, a spacecraft pose estimation network based on multi-frequency feature interaction and dynamic fusion is proposed. Backbone networks with different receptive fields are first used to separately extract spatial high-frequency features, such as semantic and edge details, and spatial low-frequency features, such as global structural information. A Transformer-based feature interaction mechanism is then used to perform self-attention and cross-attention between the two types of features, thereby aggregating long-range contextual information. To further exploit rich frequency-domain representations, a frequency-guided feature module is introduced to dynamically fuse multi-frequency features. Extensive experiments on spacecraft pose estimation datasets demonstrate that the proposed method achieves competitive performance and strong generalization capability compared with existing methods. Objective The local receptive fields of CNN operators limit their ability to capture long-range dependencies. By contrast, Transformers with multi-head self-attention are more effective in globally modeling long-range contextual information and are well suited to multiscale targets and small spacecraft at long distances. Therefore, improving the extraction and interaction of spatial high- and low-frequency features is essential for increasing the accuracy of spacecraft pose estimation. Methods A 6D pose estimation network based on Transformer-based multi-frequency feature interaction and dynamic fusion is proposed. The framework consists of two branches. A ResNet-18 branch is used to extract spatial high-frequency features, including semantic and edge information, which are also used for spacecraft semantic segmentation. A DarkNet-53 branch is used to extract spatial low-frequency features that emphasize global structural information. A Transformer-based feature interaction module is then designed to model interactions between the high- and low-frequency features through self-attention and cross-attention. This mechanism aggregates long-range contextual information, increases the diversity of feature representations for texture-poor spacecraft images, and facilitates network optimization. A frequency-guided dynamic feature fusion module is further introduced to integrate the multi-frequency features. Results and Discussions The proposed method estimates spacecraft 6D pose through multi-frequency feature interaction and dynamic fusion. On the SwissCube dataset, it achieves an overall ADI-0.1d accuracy of 82.31%, exceeding those of CA-SpaceNet (79.39%) and WDR* (78.78%). On the SPEED dataset, the combined error e_q + e_t is 0.0299, which is 22.3% lower than that of CA-SpaceNet (0.0385) and 25.2% lower than that of WDR* (0.0400). The qualitative results in Fig. 5 and Fig. 7 show that the predicted keypoints are closer to the ground truth, particularly for challenging images dominated by low-frequency content and affected by complex backgrounds. Ablation experiments further show that the feature interaction module alone increases the overall accuracy from 79.39% to 80.67%, whereas the addition of frequency-guided dynamic fusion further increases it to 82.31%. These results demonstrate that the proposed framework improves the robustness of spacecraft pose estimation and provides more reliable pose predictions for on-orbit servicing and spacecraft rendezvous and docking. Conclusions To improve the accuracy of spacecraft 6D pose estimation under challenging space imaging conditions, a multi-frequency feature interaction mechanism and a semantic segmentation branch using semantic and edge information are introduced. Long-range contextual information from spatial high- and low-frequency features is aggregated to improve feature representation. In addition, a frequency-guided dynamic feature fusion module is introduced to exploit rich frequency-domain representations. Comprehensive comparisons with mainstream methods on the SwissCube and SPEED datasets demonstrate that the proposed method improves feature representation and increases the robustness of spacecraft pose estimation under complex interference.
- On-orbit manipulation,
- Rendezvous and docking,
- Pose estimation,
- Feature interaction,
- Multi-frequency features

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References(27)

References

[1]	LIU Yating, QI Zhaoshuai, CHEN Pulin, et al. TAP-Track: Generalizable spacecraft pose tracking by tracking any points[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5630713. doi: 10.1109/TGRS.2025.3584925.
[2]	JIANG Cuicui, GUO Pengyu, HU Qinglei, et al. Uncooperative spacecraft pose estimation based on intensity and range images fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 5028910. doi: 10.1109/TIM.2024.3441021.
[3]	ZHONG Lijun, CHEN Shengpeng, WANG Wei, et al. Uncooperative spacecraft pose estimation with normalized segmentation coordinate space[J]. IEEE/ASME Transactions on Mechatronics, 2025, 30(3): 2293–2304. doi: 10.1109/TMECH.2024.3442570.
[4]	LIU Zibin, GUAN Banglei, SHANG Yang, et al. Stereo event-based, 6-DOF pose tracking for uncooperative spacecraft[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5607513. doi: 10.1109/TGRS.2025.3530915.
[5]	GUO Pengyu, JIANG Cuicui, LONG Chenrong, et al. Noncooperative spacecraft pose measurement without prior knowledge based on SAM2[J]. IEEE Transactions on Instrumentation and Measurement, 2026, 75: 5001311. doi: 10.1109/TIM.2026.3654753.
[6]	HAN Bing, WANG Chenxi, ZHANG Xinyu, et al. Pose estimation and neural implicit reconstruction toward noncooperative spacecraft without offline prior information[J]. IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(2): 2612–2630. doi: 10.1109/TAES.2024.3479199.
[7]	张春云, 孟昕曈, 陶陶, 等. 面向机器人螺栓装配的视觉感知与力控协同方法[J]. 电子与信息学报, 2026, 48(5): 2053–2065. doi: 10.11999/JEIT251193. ZHANG Chunyun, MENG Xintong, TAO Tao, et al. Vision-guided and force-controlled method for robotic screw assembly[J]. Journal of Electronics & Information Technology, 2026, 48(5): 2053–2065. doi: 10.11999/JEIT251193.
[8]	童伟, 张苗苗, 李东方, 等. 基于边缘辅助极线Transformer的多视角场景重建[J]. 电子与信息学报, 2023, 45(10): 3483–3491. doi: 10.11999/JEIT221244. TONG Wei, ZHANG Miaomiao, LI Dongfang, et al. Multiview scene reconstruction based on edge assisted epipolar Transformer[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3483–3491. doi: 10.11999/JEIT221244.
[9]	陈丹, 陈浩, 王子晨, 等. 多层ICP闭环检测下的误差状态卡尔曼滤波多模态融合SLAM[J]. 电子与信息学报, 2025, 47(5): 1517–1528. doi: 10.11999/JEIT240980. CHEN Dan, CHEN Hao, WANG Zichen, et al. Error state Kalman filter multimodal fusion SLAM based on MICP closed-loop detection[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1517–1528. doi: 10.11999/JEIT240980.
[10]	WANG Jinghao, LI Zhang, SUN Cong, et al. Satellite pose set estimation by uncertainty-guided conformal keypoint detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(12): 20120–20132. doi: 10.1109/TNNLS.2025.3598481.
[11]	PROENÇA P F and GAO Yang. Deep learning for spacecraft pose estimation from photorealistic rendering[C]. 2020 IEEE International Conference on Robotics and Automation, Paris, France, 2020: 6007–6013. doi: 10.1109/ICRA40945.2020.9197244.
[12]	SUN Han, ZHOU Zhenning, WANG Yizhao, et al. FGCT6D: Frequency-guided CNN-Transformer fusion network for metal parts’ robust 6D pose estimation[J]. IEEE Robotics and Automation Letters, 2024, 9(5): 4385–4392. doi: 10.1109/LRA.2024.3381016.
[13]	PAVLAKOS G, ZHOU Xiaowei, CHAN A, et al. 6-DoF object pose from semantic keypoints[C]. IEEE International Conference on Robotics and Automation, Singapore, Singapore, 2017: 2011–2018. doi: 10.1109/ICRA.2017.7989233.
[14]	SHARMA S, VENTURA J, and D’AMICO S. Robust model-based monocular pose initialization for noncooperative spacecraft rendezvous[J]. Journal of Spacecraft and Rockets, 2018, 55(6): 1414–1429. doi: 10.2514/1.A34124.
[15]	AUGENSTEIN S and ROCK S M. Improved frame-to-frame pose tracking during vision-only SLAM/SFM with a tumbling target[C]. 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 2011: 3131–3138. doi: 10.1109/ICRA.2011.5980232.
[16]	PARK T H, SHARMA S, and D'AMICO S. Towards robust learning-based pose estimation of noncooperative spacecraft[EB/OL]. https://doi.org/10.48550/arXiv.1909.00392, 2019.
[17]	CHEN Bo, GAO Jiewei, PARRA A, et al. Satellite pose estimation with deep landmark regression and nonlinear pose refinement[C]. 2019 IEEE/CVF International Conference on Computer Vision Workshop, Seoul, Korea (South), 2019: 2816–2824. doi: 10.1109/ICCVW.2019.00343.
[18]	SUN Ke, XIAO Bin, LIU Dong, et al. Deep high-resolution representation learning for human pose estimation[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 5686–5696. doi: 10.1109/CVPR.2019.00584.
[19]	HU Yinlin, SPEIERER S, JAKOB W, et al. Wide-depth-range 6D object pose estimation in space[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 15865–15874. doi: 10.1109/CVPR46437.2021.01561.
[20]	WANG Shunli, WANG Shuaibing, JIAO Bo, et al. Counterfactual analysis for 6D pose estimation in space[C]. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyoto, Japan, 2022: 10627–10634. doi: 10.1109/IROS47612.2022.9981172.
[21]	WANG Zi, ZHANG Zhuo, SUN Xiaoliang, et al. Revisiting monocular satellite pose estimation with transformer[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(5): 4279–4294. doi: 10.1109/TAES.2022.3161605.
[22]	CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]. The 16th European Conference on Computer Vision, Glasgow, UK, 2020: 213–229. doi: 10.1007/978-3-030-58452-8_13.
[23]	LIU Fengyi, ZHANG Zhujun, and LI Sijue. DTSE-SpaceNet: Deformable-transformer-based single-stage end-to-end network for 6-D pose estimation in space[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(3): 2555–2571. doi: 10.1109/TAES.2023.3332075.
[24]	DING Yikang, YUAN Wentao, ZHU Qingtian, et al. TransMVSNet: Global context-aware multi-view stereo network with Transformers[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 8575–8584. doi: 10.1109/CVPR52688.2022.00839.
[25]	QIN Zequn, ZHANG Pengyi, WU Fei, et al. FcaNet: Frequency channel attention networks[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 763–772. doi: 10.1109/ICCV48922.2021.00082.
[26]	HU Yinlin, HUGONOT J, FUA P, et al. Segmentation-driven 6D object pose estimation[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 3380–3389. doi: 10.1109/CVPR.2019.00350.
[27]	KISANTAL M, SHARMA S, PARK T H, et al. Satellite pose estimation challenge: Dataset, competition design, and results[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(5): 4083–4098. doi: 10.1109/TAES.2020.2989063.