Adaptive Importance Caching for Many-Light Rendering.
Bibliographic record
Abstract
Importance sampling of virtual point lights (VPLs) is an efficient method for computing global illumination. The\nkey to importance sampling is to construct the probability function, which is used to sample the VPLs, such that it\nis proportional to the distribution of contributions from all the VPLs. Importance caching records the contributions\nof all the VPLs at sparsely distributed cache points on the surfaces and the probability function is calculated by\ninterpolating the cached data. Importance caching, however, distributes cache points randomly, which makes it\ndifficult to obtain probability functions proportional to the contributions of VPLs where the variation in the VPL\ncontribution at nearby cache points is large. This paper proposes an adaptive cache insertion method for VPL\nsampling. Our method exploits the spatial and directional correlations of shading points and surface normals to\nenhance the proportionality. The method detects cache points that have large variations in their contribution from\nVPLs and inserts additional cache points with a small overhead. In equal-time comparisons including cache point\ngeneration and rendering, we demonstrate that the images rendered with our method are less noisy compared to\nimportance caching.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".