Depth from Defocus via Active Quasi-random Point Projections
Bibliographic record
Abstract
Depth sensing has many practical applications in vision-relatedtasks. While many different depth measurement techniques existand depth camera technologies are constantly being advanced, activedepth sensing still rely on specialized hardware that are highlycomplex and costly. Motivated by this, we present a novel techniquefor inferring depth measurements via depth from defocus usingactive quasi-random point projection patterns. A quasi-randompoint projection pattern is projected onto the scene of interest, andeach projection point in the image captured by a camera is analysedusing a calibration model to estimate the depth at that point.The proposed method has a relatively simple setup, consisting of acamera and a projector, and enables depth inference from a singlecapture. Furthermore, the use of a quasi-random projection patterncan allow us to leverage compressive sensing theory to producefull depth maps in future applications. Experimental resultsshow the proposed system has strong potential for enabling activedepth sensing in a simple, efficient manner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".