Bibliographic record
Abstract
A parameterization decouples the resolution of a signal on a surface from the resolution of the surface geometry. In practice, parameterized signals are conveniently and efficiently stored as texture images. Unfortunately, seams are inevitable when parametrizing most surfaces. Their visual artifacts are well known for color signals, but become even more egregious when geometry or displacement signals are used: cracks or gaps may appear in the surface. To make matters worse, parameterizations and their seams are frequently ignored during mesh processing. Carefully accounting for seams in one phase may be nullified by the next. The existing literature on seam-elimination requires non-standard rendering algorithms or else overly restricts the parameterization and signal. We present seam-aware mesh processing techniques. For a given fixed mesh, we analytically characterize the space of seam-free textures as the null space of a linear operator. Assuming seam-free textures, we describe topological and geometric conditions for seam-free edge-collapse operations. Our algorithms eliminate seam artifacts in parameterized signals and decimate a mesh---including its seams---while preserving its parameterization and seam-free appearance. This allows the artifact-free display of surface signals---color, normals, positions, displacements, linear blend skinning weights---with the standard GPU rendering pipeline. In particular, our techniques enable crack-free use of the tessellation stage of modern GPU's for dynamic level-of-detail. This decouples the shape signal from mesh resolution in a manner compatible with existing workflows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.154 | 0.087 |
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".