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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.00147 |
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| _version_ | 1866915971684368384 |
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| author | Gopu, Bala Prenith Reddy Quinn, Patrick Nehma, George M. Tiwari, Madhur Ueckermann, Matt Hinckley, David McKenna, Christopher |
| author_facet | Gopu, Bala Prenith Reddy Quinn, Patrick Nehma, George M. Tiwari, Madhur Ueckermann, Matt Hinckley, David McKenna, Christopher |
| contents | On-orbit inspection imagery is crucial as it enables characterization of non-cooperative resident space objects, providing the geometry and structural condition essential for active debris removal and on-orbit servicing mission planning. However, most existing neural implicit surface reconstruction methods have been confined to synthetic or hardware-in-the-loop data with known camera poses and controlled illumination. In this work, we present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery. We demonstrate it on publicly released ISS inspection footage from the STS-119 mission and publicly released on-orbit inspection footage of an H-IIA rocket upper stage. We find that segmentation-based background removal is essential for successful camera pose estimation from real on-orbit footage, where background variation between frames caused direct processing to fail entirely. We further incorporate photometric correction of per-frame exposure variations and analyze its behavior across datasets, finding that performance in shadowed regions varies with the illumination characteristics of the input footage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00147 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects Gopu, Bala Prenith Reddy Quinn, Patrick Nehma, George M. Tiwari, Madhur Ueckermann, Matt Hinckley, David McKenna, Christopher Computer Vision and Pattern Recognition On-orbit inspection imagery is crucial as it enables characterization of non-cooperative resident space objects, providing the geometry and structural condition essential for active debris removal and on-orbit servicing mission planning. However, most existing neural implicit surface reconstruction methods have been confined to synthetic or hardware-in-the-loop data with known camera poses and controlled illumination. In this work, we present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery. We demonstrate it on publicly released ISS inspection footage from the STS-119 mission and publicly released on-orbit inspection footage of an H-IIA rocket upper stage. We find that segmentation-based background removal is essential for successful camera pose estimation from real on-orbit footage, where background variation between frames caused direct processing to fail entirely. We further incorporate photometric correction of per-frame exposure variations and analyze its behavior across datasets, finding that performance in shadowed regions varies with the illumination characteristics of the input footage. |
| title | From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.00147 |