Triton: Practical pre-computed sound propagation for games and virtual reality
Bibliographic record
Abstract
Triton is a pre-computed wave-based acoustics system recently shipped in the game “Gears of War 4.” Games and VR present exciting new opportunities for virtual acoustics by providing the player with scene-dependent reverberation cues and conveying information about visually occluded areas. Several technical challenges must be met. Scenes containing millions of polygons are common, with mixed indoor-outdoor spaces like broken buildings, courtyards, caves, and rocks. A viable technique must handle this complex visual geometry without users' intervention. The emphasis is on ensuring the resulting auralization is perceptually convincing, varying smoothly on source and listener motion in such scenes. Highly occluded cases with salient paths undergoing multiple edge-diffraction and scattering are common. Computational requirements are quite stringent. A fraction of a single CPU core must be used for acoustic calculations for many tens of moving sources. We discuss how these challenges shape Triton’s design. Pre-computation is used to minimize runtime cost. Wave simulation provides complete automation for complex scene geometry. The produced fields contain billions of responses that take terabytes of memory. A key contribution is compact encoding of this data in less than hundred megabytes: objective room acoustic parameters are approached from a novel perspective to aid in spatial compression. The resulting parametric framework is fast and practical for current games and VR applications. Video demonstrations will be shown.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".