HARDWARE-ACCELERATED PARALLEL-SPLIT SHADOW MAPS
Why this work is in the frame
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Bibliographic record
Abstract
Shadow mapping is well known for its generality and efficiency, thus it has been extensively employed for real-time shadow rendering in diverse applications. However, it suffers from inherent aliasing problem due to its image-based nature. In this paper, we present the parallel-split shadow maps scheme which produces high-quality shadows especially in large-scale and complex scenes. Our scheme splits the view frustum into parts using planes parallel to the view plane, and then generates a shadow map for each part. A fast and robust splitting strategy based on the analysis of shadow-map aliasing is proposed, which results in a moderate aliasing distribution over the depth range. Hardware-accelerated processing is developed to eliminate extra rendering passes which surpass that of standard shadow mapping when synthesizing scene-shadows.
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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 | 0.001 | 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 it