Bibliographic record
Abstract
This paper addresses the problem of computing the Euclidean 3D structure of an observed scene. Given at least 2 images with pixel correspondences, the 3D structure of the scene and the motion of the camera (translation and rotation) are calculated simultaneously. We study here the effect of inaccurate intrinsic parameters on the quality of the recovered reconstruction. Classical methods based on the essential matrix computation have proven to be very unstable when the intrinsic parameters of the cameras are not known exactly. To overcome such unstability, we used a method where a particular choice of a 3D Euclidean coordinate system with a different parameterization of the motion/structure problem allowed us to reduce significantly the total number of unknowns. In addition, the simultaneous calculation of the camera motion and the 3D structure has made the computation of the motion and structure less sensitive to the errors in the values of the intrinsic parameters of the camera. Experiments with real images validated our method and experiments with simulated data showed how the errors on the intrinsic parameters affect the accuracy of the reconstruction.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".