Comparison of matching criteria for the interposition problem in augmented reality
Why this work is in the frame
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Bibliographic record
Abstract
The article discusses the interposition problem in augmented reality-that is, the realistic and physically-consistent embedding of a 3D virtual object (3DVO) into a real scene. Our method deals with stereoscopic views of an unstructured and unknown real scene and avoids explicit 3D reconstruction. Instead, we consider a 3DVO as a set of vertices and rely on local stereo matching to decide whether each vertex should be visible or not. We focus on the choice of an optimal matching criterion in the particular context of interposition in augmented reality. After a brief presentation of our innovative solution, we review classical matching spaces and criteria, and select the most appropriate for our task. We compare and interpret their performance against parameters such as noise level and matching window size and present preliminary results of interposition for real scenes.
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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