The Reverse Projection Correlation Principle for Depth from Defocus
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Bibliographic record
Abstract
In this paper, we address the problem of finding depth from defocus in a fundamentally new way. Most previous methods have used an approximate model in which blurring is shift invariant and pixel area is negligible. Our model avoids these assumptions. We consider the area in the scene whose radiance is recorded by a pixel on the sensor, and relate the size and shape of that area to the scene's position with respect to the plane of focus. This is the notion of reverse projection, which allows us to illustrate that, when out of focus, neighboring pixels will record light from overlapping regions in the scene. This overlap results in a measurable change in the correlation between the pixels' intensity values. We demonstrate that this relationship can be characterized in such a way as to recover depth from defocused images. Experimental results show the ability of this relationship to accurately predict depth from correlation measurements.
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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