Efficiently Simulating the Bokeh of Polygonal Apertures in a Post‐Process Depth of Field Shader
Bibliographic record
Abstract
Abstract The effect of aperture shape on an image, known in photography as ‘bokeh’, is an important characteristic of depth of field in real‐world cameras. However, most real‐time depth of field techniques produce Gaussian bokeh rather than the circular or polygonal bokeh that is almost universal in real‐world cameras. ‘Scattering’ (i.e. point‐splatting) techniques provide a flexible way to model any aperture shape, but tend to have prohibitively slow performance, and require geometry‐shaders or significant engine changes to implement. This paper shows that simple post‐process ‘gathering’ depth of field shaders can be easily extended to simulate certain bokeh effects. Specifically we show that it is possible to efficiently model the bokeh effects of square, hexagonal and octagonal apertures using a novel separable filtering approach. Performance data from a video game engine test demonstrates that our shaders attain much better frame rates than a naive non‐separable approach.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".