Using Stochastic Sampling to Create Depth-of-Field Effect in Real-Time Direct Volume Rendering
Bibliographic record
Abstract
Real-time visualization of volumetric data is increasingly used by physicians and scientists. Enhanced depth perception in Direct Volume Rendering (DVR) plays a crucial role in applications such as clinical decision making. Our goal is to devise a flexible blurring method in DVR and ultimately improve depth perception in real-time DVR using synthetic depth of field (DoF) effect. We devised a permutation-based stochastic sampling method for ray casting to render images with DoF effect. Our method uses 2D blurring kernels in 3D space for each sample on a ray. Furthermore, we reduce the number of required samples for each kernel of size n 2 from n 2 to only 2 samples. This method is flexible and can be used for DoF, focus-context blurring, selective blurring, and potentially for other photographic effects such as the tilt effect.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".