3D Model Creation Using Self-Identifying Markers and SIFT Keypoints
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".