Exploiting Coherence and Data-driven Models for Real-time Global Illumination
Bibliographic record
Abstract
Realistic computer generated images are computed by combining geometric effects, reflectance models for several captured and phenomenological materials, and real-world lighting according to mathematical models of physical light transport. Several important lighting phenomena should be considered when targeting realistic image simulation.\n\nA combination of soft and hard shadows, which arise from the interaction of surface and light geometries, provide necessary shape perception cues for a viewer. A wide variety of realistic materials, from physically-captured reflectance datasets to empirically designed mathematical models, modulate the virtual surface appearances in a manner that can further dissuade a viewer from considering the possibility of computational image synthesis over that of reality. Lastly, in many important cases, light reflects off many different surfaces before entering the eye. These secondary effects can be critical in grounding the viewer in a virtual world, since the human visual system is adapted to the physical world, where such effects are constantly in play.\n\nSimulating each of these effects is challenging due to their individual underlying complexity. The net complexity is compounded when several effects are combined. This thesis will investigate real-time approaches for simulating these effects under stringent performance and memory constraints, and with varying degrees of interactivity.\n\nIn order to make these computations tractable given these added constraints, I will use data and signal analysis techniques to identify predictable patterns in the different spatial and angular signals used during image synthesis. The results of this analysis will be exploited with several analytic and data-driven mathematical models that are both efficient, and yield accurate approximations with predictable and controllable error.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".