Adaptive multiple texture approach to texture packing for 3D video games
Bibliographic record
Abstract
This paper presents an adaptive multiple texture approach to the problem of texture packing for 3D video games. In modern graphics hardware, texture size is typically constrained to width and height dimensions that are powers of two. To reduce the texture management overhead caused by storing individual textures, texture packing algorithms are used to pack multiple textures into a single powers-of-two texture. Current texture packing techniques are very limiting as they are capable of packing textures only into a single texture of predefined size. This can result in significant wasted texture space due to the powers-of-two texture size restrictions. In the proposed technique, individual arbitrarily sized rectangular textures are packed into multiple textures in an adaptive manner. This approach reduces the amount of wasted texture space in a more efficient manner by adaptively determining the quantity as well as size of textures being used during the packing process. Experimental results demonstrate the effectiveness of this technique in packing textures in an efficient and automated fashion. This makes it well suited for improving texture management in future 3D video games, where resources are limited and a high frame rate needs to be achieved to provide a truly immersive experience.
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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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".