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Record W1969668713 · doi:10.1145/1268517.1268521

Twinned meshes for dynamic triangulation of implicit surfaces

2007· article· en· W1969668713 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRendering (computer graphics)Polygon meshComputer scienceSurface triangulationComputer graphics (images)TriangulationCurvatureBowyer–Watson algorithmMinimum-weight triangulationComputer visionDelaunay triangulationAlgorithmGeometryMathematics

Abstract

fetched live from OpenAlex

We introduce a new approach to mesh an animated implicit surface for rendering. Our contribution is a method which solves stability issues of implicit triangulation, in the scope of real-time rendering. This method is robust, moreover it provides interactive and quality rendering of animated or manipulated implicit surfaces. This approach is based on a double triangulation of the surface, a mechanical one and a geometric one. In the first triangulation, the vertices are the nodes of a simplified mechanical finite element model. The aim of this model is to uniformly and dynamically sample the surface. It is robust, efficient and prevents the inversion of triangles. The second triangulation is dynamically created from the first one at each frame. It is used for rendering and provides details in regions of high curvature. We demonstrate this technique with skeleton-based and volumetric animated surfaces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.312
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it