Mesh Parameterization Methods and Their Applications
Bibliographic record
Abstract
Parameterizations between surface meshes and a variety of domains have numerous applications in computer graphics and geometry processing. In recent years numerous methods for parameterizing meshes have been developed, targeting diverse parameter domains and focusing on different parameterization properties. Mesh Parameterization Methods and their Applications reviews the various parameterization methods, summarizing the main ideas of each technique and focusing on the practical aspects of the methods. It also provides examples of the results generated by many of the more popular methods. When several methods address the same parameterization problem, the survey strives to provide an objective comparison between them based on criteria such as parameterization quality, efficiency and robustness. Mesh Parameterization Methods and their Applications provides guidance to researchers and developers when assessing the suitability of different methods for various applications. The authors focus on the practical aspects of the methods available, such as time complexity and robustness. They also provide multiple examples of parameterizations generated using different methods, allowing the reader to visually evaluate and compare the results.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".