Parametric Foveation for Progressive Texture and Model Transmission
Bibliographic record
Abstract
Spatially varying sensing (foveation) has been used in many different areas of Computer Vision, such as image compression and video teleconferencing and in perceptually driven Level of Detail (LOD) representations in graphics. In this work, we show that foveation is advantageous for interactive mesh and texture transmission in online 3D applications. Unlike traditional mesh representations where all 3D vertices need to be transmitted, we only need to transmit a collection of points-of-interest (foveae) and information on only one (rather than three) axis. Thereby, we can achieve a threefold reduction in the amount of data that needs to be transmitted to represent a new 3D model. Our research differs from level of detail (LOD) based approaches using perceptually driven simplification in that (i) the mesh and texture resolutions vary smoothly and continuously in our approach compared to distinct levels of details in adjoining regions in other foveated or multiresolution LOD based methods; and (ii) the approach works for an integrated foveated texture and mesh representation. The current implementation extends our past research in image and video compression [1] and is restricted to regular grid mesh representation produced by 3D scanners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".