Extended Fractional View Integral Photography Using Slanted Orthogonal Lenticular Lenses
Bibliographic record
Abstract
Integral photography (IP) is one of the best 3D image display systems because both horizontal and vertical parallaxes can be obtained without a need to wear stereo glasses. IP hardware can most easily be produced by placing a fly's eye lens on a high-definition flat panel display, such as a liquid crystal display (LCD). The price of high-definition LCDs is falling given the progress of its production technology. By contrast, the initial cost of producing a fly's eye lens remains very high because a very expensive metal mold has to be produced in most cases. This problem has been solved by introducing the extended fractional view method, which enables the combination of any ready-made LCD with any ready-made fly's eye lens. However, other problems persist, i.e., the limited types of ready-made fly's eye lens on the market and the lack of large fly's eye lenses. Without such lenses, the 3D display screen size cannot be enlarged. In this work, we solved this problem by replacing a fly's eye lens with two mutually perpendicular lenticular lenses based on the fact that two orthogonally stacked lenticular lenses work as if they were a single fly's eye lens. Unlike fly's eye lenses, large off-theshelf lenticular lenses are available in the market because of their applications in large 3D signboards, among others. Another issue to be addresses is the suppression of very obstructive moir pattern caused by the interference between the LCD of pixels and small convex lenses. This problem was solved by slightly tilting the orthogonal fly's eye lenses against the LCD. In our experiment, a 3D scene data created with The Persistence of Vision Raytracer (POV-Ray) was rendered from 16 16 different viewpoints so that 256 still images were obtained. Our original software, which was developed in C# language, synthesized an IP image from the 256 images. When the image was displayed on a 4K 28-inch LCD and observed through the orthogonal lenticular lenses, a 3D image with both horizontal and vertical parallaxes was observed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".