PROJECTIVE VOLUME RENDERING BY EXCLUDING OCCLUDED VOXELS
Bibliographic record
Abstract
In volume rendering, an important issue in acceleration is to reduce the calculations for occluded voxels. Although this issue has been addressed in the ray casting approach, it is difficult to apply the idea to the projection approach due to uncertain termination conditions. In this paper, we propose a new method to effectively address the exclusion problem in the projection approach, so the rendering process can be accelerated without impairing the rendered image quality. In the rendering process, this new method employs the dynamic screen technique to manage the pixels whose accumulated opacity has not reached 1.0. A ray-cast link at each pixel is set up to record the rendered voxels for the corresponding ray cast from the pixel to intersect. According to the rendered voxels covering the pixels whose accumulated opacity value is below 1.0, visible voxels are selected to render from front to back by the neighboring relationship between the rendered voxels and the voxels to be rendered. Thus, the occluded voxels are dynamically excluded from the loading and rendering processes accurately. Our proposed method can be in general applied to both parallel and perspective projections, using regular and irregular volume datasets. Our experimental results showed that the proposed method can significantly accelerate volume rendering if the data volume has a high percentage of occluded voxels. This method can also perform fairly efficiently for the expensive shading calculations if requested in volume rendering.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".