Learning Structured Distance Mappings for Spacecraft Pose Estimation with Feature Degradation.
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| Title: | Learning Structured Distance Mappings for Spacecraft Pose Estimation with Feature Degradation. |
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| Authors: | Yan, Chuan1,2,3,4 (AUTHOR), Long, Hongfeng1,2,3,4 (AUTHOR), Cao, Zifei1,2,3,4 (AUTHOR), Ma, Yuebo1,3,4 (AUTHOR), Suo, Jiayu1,3,4 (AUTHOR), Lu, Xiangying1,2,3,4 (AUTHOR), Zhao, Rujin1,3,4 (AUTHOR) zhaorj@ioe.ac.cn, Peng, Zhenming2,4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1647. 27p. |
| Subjects: | Pose estimation (Computer vision), Image stabilization |
| Abstract: | Highlights: What are the main findings? A structure-bound signed distance map (SDM) representation is proposed to encode predefined spacecraft structural lines and provide solver-ready 2-D-to-3-D line correspondences for analytic PnL pose estimation. The proposed SDMNet improves pose-estimation robustness under synthetic close-range feature degradation, including motion blur and occlusion, compared with adapted baseline architectures within the same SDM/mask prediction framework. What are the implications of the main findings? Continuous SDM-based line modeling can better preserve structural-line continuity and identity than discrete point- or line-segment observations under degraded visual conditions. The results suggest that coupling learned structure-aware geometric prediction with analytic PnL solving is a promising direction for single-frame non-cooperative spacecraft pose estimation. Pose estimation of non-cooperative spacecraft remains challenging under feature degradation. Motion blur, self-occlusion, and weak texture can cause structural line disappearance, correspondence ambiguity, and localization drift, which destabilize conventional point- and line-based analytic pose estimation pipelines relying on discrete feature detection and post-hoc 2-D-to-3-D association. To address these issues, we propose a two-stage framework for line-based 6-DoF pose estimation built upon a structure-bound multi-channel spatial distance mapping (SDM), where each SDM channel is uniquely associated with one predefined 3-D model line. By explicitly binding each SDM channel to a predefined 3-D model line, the proposed representation encodes 2-D-to-3-D line correspondence directly in the network output, thereby avoiding unstable line matching after prediction and providing solver-consistent geometric constraints for Perspective-n-Line (PnL) estimation. To reduce localization blur around the SDM zero-level set, a cross-scale self-attention (CSSA) mechanism is introduced to couple high-resolution localization features with low-resolution structural context through window-level cross-scale attention. Based on the predicted SDMs, explicit 2-D structural lines are recovered through weighted robust fitting in narrow bands around the zero-level sets, enabling the completion of partially or fully occluded lines and yielding solver-ready observations for PnL pose recovery. Experiments on a close-range non-cooperative spacecraft dataset with simulated observation distances of 10–30 m show that SDMNet achieves translation/rotation errors of 0.8%/0.0372 rad, 0.91%/0.0394 rad, and 1.38%/0.0579 rad under original, motion-blur, and occlusion conditions, respectively. These results indicate that the proposed framework can robustly recover correspondence-aware structural observations from degraded images and improve the accuracy and stability of spacecraft pose estimation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A structure-bound signed distance map (SDM) representation is proposed to encode predefined spacecraft structural lines and provide solver-ready 2-D-to-3-D line correspondences for analytic PnL pose estimation. The proposed SDMNet improves pose-estimation robustness under synthetic close-range feature degradation, including motion blur and occlusion, compared with adapted baseline architectures within the same SDM/mask prediction framework. What are the implications of the main findings? Continuous SDM-based line modeling can better preserve structural-line continuity and identity than discrete point- or line-segment observations under degraded visual conditions. The results suggest that coupling learned structure-aware geometric prediction with analytic PnL solving is a promising direction for single-frame non-cooperative spacecraft pose estimation. Pose estimation of non-cooperative spacecraft remains challenging under feature degradation. Motion blur, self-occlusion, and weak texture can cause structural line disappearance, correspondence ambiguity, and localization drift, which destabilize conventional point- and line-based analytic pose estimation pipelines relying on discrete feature detection and post-hoc 2-D-to-3-D association. To address these issues, we propose a two-stage framework for line-based 6-DoF pose estimation built upon a structure-bound multi-channel spatial distance mapping (SDM), where each SDM channel is uniquely associated with one predefined 3-D model line. By explicitly binding each SDM channel to a predefined 3-D model line, the proposed representation encodes 2-D-to-3-D line correspondence directly in the network output, thereby avoiding unstable line matching after prediction and providing solver-consistent geometric constraints for Perspective-n-Line (PnL) estimation. To reduce localization blur around the SDM zero-level set, a cross-scale self-attention (CSSA) mechanism is introduced to couple high-resolution localization features with low-resolution structural context through window-level cross-scale attention. Based on the predicted SDMs, explicit 2-D structural lines are recovered through weighted robust fitting in narrow bands around the zero-level sets, enabling the completion of partially or fully occluded lines and yielding solver-ready observations for PnL pose recovery. Experiments on a close-range non-cooperative spacecraft dataset with simulated observation distances of 10–30 m show that SDMNet achieves translation/rotation errors of 0.8%/0.0372 rad, 0.91%/0.0394 rad, and 1.38%/0.0579 rad under original, motion-blur, and occlusion conditions, respectively. These results indicate that the proposed framework can robustly recover correspondence-aware structural observations from degraded images and improve the accuracy and stability of spacecraft pose estimation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101647 |
