An algorithm for polygon subdivision based on vertex normals
Bibliographic record
Abstract
In order to achieve the impression of a smooth surface while rendering a polygon mesh, normal vector vectors may be provided in the vertices of the mesh that are the average of the surface normals of the adjacent polygons. Interpolation of these normal vectors while rendering of the polygons in the mesh, and using the interpolated normal vectors in the shading computations, yields a smoothly varying intensity distribution. There is an inherent mismatch, however, between the smoothness of the shading thus achieved and the non-smoothness of the geometry which is particularly visible at silhouettes, showing as straight edges and non-smooth edge junctions at the silhouette vertices. A remedy for these artefacts is suggested. The remedy consists of subdividing each input polygon into a mesh of polygons prior to rendering. The shape of this resulting polygon mesh is controlled by the normal vectors that are provided in the vertices of the original polygon, unlike other subdivision schemes that make use of adjacent polygons. With the method, polygons equipped with vertex normal vectors can therefore be processed without further knowledge of neighbour polygons. This makes the method well-suited in the context of graphics libraries, such as OpenGl, that treat polygons typically on a per-polygon basis. So the proposed computation of the mesh which replaces the original polygon can be viewed as a filter which may operate as a process in front of a traditional polygon rendering pipeline.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".