3D Model Creation Using Self-Identifying Markers and SIFT Keypoints
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
3D object modeling can be accomplished using fiducial markers and/or feature detectors. Fiducial markers provide high reliability of detection, however, it is undesirable to cover an object to be modeled with markers. Feature detectors can find correspondences between images but they cannot always be relied on to be usable for camera localization. A method is shown that uses the strengths of both to automatically create 3D models of object as well as simultaneously calibrating the camera. Self-identifying fiducial markers are used in arrays to localize the camera pose for each image and SIFT features are used to find and match object features between images. Tetrahedrons formed by Delaunay triangulation of the 3D SIFT points are carved to the model. A system is shown where 3D models are generated automatically of an object placed on a marker array simply by capturing a set of images from uncontrolled locations from a camera with unknown intrinsic parameters
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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) | 0.000 | 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