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Record W2024748549 · doi:10.1109/3dpvt.2006.122

Revealing Significant Medial Structure in Polyhedral Meshes

2006· article· en· W2024748549 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsEuclidean geometryPolygon meshMedial axisComputer graphicsComputer scienceComputationSoundnessComputational geometryHomogeneous spaceGeometric modelingLattice (music)MathematicsAlgorithmGeometryArtificial intelligenceComputer graphics (images)

Abstract

fetched live from OpenAlex

Medial surfaces are popular representations of 3D objects in vision, graphics and geometric modeling. They capture relevant symmetries and part hierarchies and also allow for detailed differential geometric information to be recovered. However, exact algorithms for their computation from meshes must solve high-order polynomial equations, while approximation algorithms rarely guarantee soundness and completeness. In this article we develop a technique for computing the medial surface of an object with a polyhedral boundary, which is based on an analysis of the average outward flux of the gradient of its Euclidean distance function. This analysis leads to a coarse-to-fine algorithm implemented on a cubic lattice that reveals at each iteration the salient manifolds of the medial surface. We provide comparative results against a state-of-the-art method in the literature.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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