Rectification on uncalibrated epipolar stereo images and dense disparity map
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a result of the studies to generate the dense disparity map based on the epipolar geometry of stereo vision. The distance (missing depth in 2D images) to a point on the objects recorded in a pair of stereo images can be estimated from the disparity due to the parallax between two cameras. The well established theories of the epipolar geometry combined with the robust method of RANSAC to calculate the fundamental matrix was implemented. Stereo images are rectified to make epipolar lines parallel along the horizontal axis (raster lines), and to enable 1D search for matching the points of correspondence. This paper proposes a scanning type pattern matching method that can be used to generate a dense disparity map which displays a 2D distribution of pixel-by-pixel disparities. Preliminary results are presented.
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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 | 0.001 |
| 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