Exploring height fields: interactive visualization and applications
Bibliographic record
Abstract
Height fields are an important modeling and visualization tool in many applications and their exploration requires their display at interactive frame rates. This is hard to achieve even with high performance graphics computers due to their inherent geometric complexity. Typical solutions consist of using polygonal approximations of the height field to reduce the number of geometric primitives that need to be rendered. Starting from a rough approximation, a refinement process is operated until a desired level of detail is reached. In this work, we present a novel efficient algorithm that starts with an approximation that carries enough information about the height field so that only few refinement steps are needed to achieve any desired level of detail. Our initial approximation is a simple triangulation whose nodes are the critical points of the height field, that is the peaks, pits, and passes of the surface which give its overall shape. The extraction of critical points of the surface, which is a discrete structure, is done using a newly designed algorithm based on discrete Morse theory and computational homology algorithms. <sup>1-3</sup>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".