Automatic Correspondences for Photogrammetric Model Building
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 problem of building geometric models has been a central application in photogrammetry. Our goal is to partially automate this process by finding the features necessary for computing the exterior orientation. This is done by robustly computing the fundamental matrix, and trilinear tensor for all images pairs and some image triples. The correspondences computed from this process are chained together and sent to a commercial bundle adjustment program to find the exterior camera parameters. To find these correspondences it is not necessary to have camera calibration, nor to compute a full projective reconstruction. Thus our approach can be used with any photogrammetric model building package. We also use the computed projective quantities to autocalibrate the focal length of the camera. Once the exterior orientation is found, the user still needs to manually create the model, but this is now a simpler process.
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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 | 0.000 |
| 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) | 0.001 | 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 it