Automatic feature‐preserving size field for three‐dimensional mesh generation
Bibliographic record
Abstract
Abstract This article presents a methodology aiming at easing considerably the generation of high‐quality meshes for complex three‐dimensional (3D) domains. To this end, a mesh size field h ( x ) is computed, taking surface curvatures and geometric features into account. The size field is tuned by five intuitive parameters and yields quality meshes for arbitrary geometries. Mesh size is initialized on a surface triangulation of the domain based on discrete curvatures and medial axis transform computations. It is then propagated into the volume while ensuring the size gradient ∇ h is controlled so as to obtain a smoothly graded mesh. As the size field is stored in an independent octree data structure, it can be computed separately, then plugged into any mesh generator able to respect a prescribed size field. The procedure is automatic, in the sense that minimal interaction with the user is required. Applications of our methodology on CAD models taken from the very large ABC dataset are presented. In particular, all presented meshes were obtained with the same generic set of parameters, demonstrating the universality of the technique.
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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 | 0.004 |
| 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 | 0.001 | 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 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".