Bibliographic record
Abstract
Stereoscopic disparity plays an important role in the processing and compression of 3D imagery. For example, dense disparity fields are used to reconstruct intermediate images. Although for small camera baselines dense disparity can be reliably estimated using gradient-based methods, this is not the case for large baselines due to the violation of underlying assumptions. Block matching algorithms work better but they are likely to get trapped in a local minimum due to the increased search space. An appropriate method to estimate large disparities is by using feature points. However, since feature points are unique, they are also sparse. In this paper, we propose a disparity estimation method that combines the reliability of feature-based correspondence methods with the resolution of dense approaches. In the first step we find feature points in the left and right images using Harris operator. In the second step, we select those feature points that allow one-to-one left-right correspondence based on a cross-correlation measure. In the third step, we use the computed correspondence points to control the computation of dense disparity via regularized block matching that minimizes matching and disparity smoothness errors. The approach has been tested on several large-baseline stereo pairs with encouraging initial results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".