Stereo-vision-based 3D modeling of space structures
Bibliographic record
Abstract
Servicing satellites in space requires accurate and reliable 3D information. Such information can be used to create virtual models of space structures for inspection (geometry, surface flaws, and deployment of appendages), estimation of relative position and orientation of a target spacecraft during autonomous docking or satellite capture, replacement of serviceable modules, detection of unexpected objects and collisions. Existing space vision systems rely on assumptions to achieve the necessary performance and reliability. Future missions will require vision systems that can operate without visual targets and under less restricted operational conditions towards full autonomy. Our vision system uses stereo cameras with a pattern projector and software to obtain reliable and accurate 3D information. It can process images from cameras mounted on a robotic arm end-effector on a space structure or a spacecraft. Image sequences can be acquired during relative camera motion, during fly-around of a spacecraft or motion of the arm. The system recovers the relative camera motion from the image sequence automatically without using spacecraft or arm telemetry. The 3D data computed can then be integrated to generate a calibrated photo-realistic 3D model of the space structure. Feature-based and shape-based approaches for camera motion estimation have been developed and compared. Imaging effects on specular surfaces are introduced by space materials and illumination. With a pattern projector and redundant stereo cameras, the robustness and accuracy of stereo matching are improved as inconsistent 3D points are discarded. Experiments in our space vision facility show promising results and photo-realistic 3D models of scaled satellite replicas are created.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".