Anisotropic adaptive method for triangular meshes smoothing
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Bibliographic record
Abstract
Even with the advancing laser scanner technology, the digital objects are inevitably corrupted with random noise during the acquisition process. Many algorithms, regardless of principle, share the same basic idea of noise reduction through mesh smoothing. Smoothing can be performed locally, as in the anisotropic filtering by calculus of variations; or by smoothing mesh attributes such as normal vector and then adjusting vertex positions. In this study, the authors propose a novel algorithm based on the statistical distribution noise for anisotropic mesh smoothing. In this approach, the probability density function of the noise is first estimated, and then the anisotropic smoothing operation is performed through the diffusion tensor that allows to preserve mesh characteristics like edges, corner and ridges. The proposed algorithm is easy to implement and is thus suitable for real‐time noise reduction application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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