Bibliographic record
Abstract
This paper addresses the problem of threedimensional reconstruction and how the geometry of the stereo vision system affects the quality of such a reconstruction. We have considered the general case of non calibrated cameras with approximately known intrinsic parameters. The latter could be known either from previous experiments or from the manufacturer's specifications. In particular, the paper aims at finding the best geometric configuration of the stereo vision system(rotation and translation between the two cameras) that is the least sensitive to errors on the intrinsic parameters. Given that in most cases, in robotics for instance, we have the freedom to set-up the geometry of the stereo vision system, finding how cameras' geometry interacts with errors from different sources is a central issue. Furthermore, when the intrinsic parameters are completely unknown, we have used a single constraint from the scene that has allowed us to calculate the focal length and therefore, the Euclidean reconstruction. We have used extensive simulations where the stereo vision geometry has ranged from pure translation to general 3D motion. The obtained results have clearly shown that a pure translation is always better, as it is not significantly affected by errors on the intrinsic parameters. When assuming that the intrinsic parameters are not known, the use of the perpendicularity constraint from the scene has made it possible to avoid the classical and tedious calibration process.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".