LOCAL SURFACE RECONSTRUCTION OF OBJECTS IN SPACE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing number of objects orbiting our planet necessitates the creation of sensing systems designed to determine the surface structure of such objects. One of the key challenges facing computer vision systems used in space is the presence of specular surfaces on most man-made orbital objects. Such surfaces present a challenge to conventional vision systems due to specular reflections, which may mask the true location of the object and hence lead to incorrect measurements. The incorporation of traditional highpowered illuminants, such as laser beams, in a space-based computer vision system can also be problematic since the instruments inside space structures may be sensitive to various forms of radiation. A properly designed computer vision system could assist in the repair and maintenance of delicate space equipment. This article describes the development of a fixed vision system which can recover the local surface structure of highly specular objects. The system utilizes a commercial trinocular stereo vision system and a low-power twodimensional illuminant. The local surface structure of an object is obtained by projecting coded light patterns onto the object. As space objects are neither fully specular nor fully diffuse, an algorithm has been developed which recovers local surface structure from both the specular and diffuse regions of an object. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it