A general optimal pixel aspect ratio model for stereo-based 3D reconstruction and visualization
Bibliographic record
Abstract
We propose a mathematical model for optimizing pixel aspect ratio for the best 3D estimation in both stereo-based 3D reconstruction and 3D viewing applications. We analyze the 3D reconstruction through a 3D display medium and reduce the whole process to a single stereo system so that a unified model can be applied to both direct reconstruction from stereo images and indirect reconstruction through stereo content presented on a 3D display. We use this unified model to extend our earlier work to a general solution for determining the optimal pixel aspect ratio for both applications. Unlike earlier work, the solution proposed here relates the optimal pixel aspect ratio to the device-specific parameters, rather than the stereo configuration parameters, which makes it more easily applicable in design and manufacture of stereo capture and display devices. In general, our mathematical model and subjective user studies suggest that, for a given total resolution, a finer horizontal discretization with a ratio of about 0.6 leads to a more accurate 3D reconstruction and a better 3D visual experience.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".