Quality metric for approximating subjective evaluation of 3-D objects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many factors, such as the number of vertices and the resolution of texture, can affect the display quality of three-dimensional (3-D) objects. When the resources of a graphics system are not sufficient to render the ideal image, degradation is inevitable. It is, therefore, important to study how individual factors will affect the overall quality, and how the degradation can be controlled given limited resources. In this paper, the essential factors determining the display quality are reviewed. We then integrate two important ones, resolution of texture and resolution of wireframe, and use them in our model as a perceptual metric. We assess this metric using statistical data collected from a 3-D quality evaluation experiment. The statistical model and the methodology to assess the display quality metric are discussed. A preliminary study of the reliability of the estimates is also described. The contribution of this paper lies in: 1) determining the relative importance of wireframe versus texture resolution in perceptual quality evaluation and 2) proposing an experimental strategy for verifying and fitting a quantitative model that estimates 3-D perceptual quality. The proposed quantitative method is found to fit closely to subjective ratings by human observers based on preliminary experimental results.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it