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Record W2058987444 · doi:10.1111/1467-8659.00684

Precise Ink Drawing of 3D Models

2003· article· en· W2058987444 on OpenAlexaff
Mário Costa Sousa, Kevin Foster, Brian Wyvill, Faramarz Samavati

Bibliographic record

VenueComputer Graphics Forum · 2003
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPolygon meshComputer scienceRendering (computer graphics)CurvatureComputer graphics (images)Non-photorealistic renderingEnhanced Data Rates for GSM EvolutionArtificial intelligenceProcess (computing)Computer visionFeature (linguistics)GeometryAnimationMathematicsComputer animationComputer facial animation

Abstract

fetched live from OpenAlex

Abstract Drawings made with precise pen strokes accurately reveal the geometric forms that give subjects their characteristicshape. We present a system for non‐photorealistic rendering of precise drawing strokes over dense 3Dtriangle meshes with arbitrary topology. During an automatic pre‐process, we construct an extended version ofthe edge‐buffer data structure to allow the calculation of shape measures at each mesh edge, by adapting numericalmethods used in geomorphology. At runtime, feature edges related to shape measures are extracted andrendered as strokes with varying thickness and pen marking styles. Stroke thickness is automatically adjusted byconsidering surface curvature. Pen marking styles and visual effects of ink distribution are both controlled by theuser. We demonstrate precise drawing strokes over complex meshes revealing a variety of shape characteristics.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.025
GPT teacher head0.268
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2003
Admission routes1
Has abstractyes

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