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Record W2200967614 · doi:10.20380/gi2015.10

Cover-it: an interactive system for covering 3d prints

2015· article· en· W2200967614 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

VenueCanada Human-Computer Communications Society · 2015
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCover (algebra)Computer scienceObject (grammar)Computer graphics (images)Heuristics3d printedAnimationComputer visionArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The ubiquity of 3D printers has made it possible to print various types of objects, from toys to mechanical objects. However, most available 3D printers are single or double colors. Even printers that can produce objects with multiple colors do not offer the ability to cover the object with a desired material, such as a piece of cloth or fur. In this paper, we propose a system that produces simple 2D patches that can be used as a reference for cutting material to cover the 3D printed object. The system allows for user interactions to correct and modify the patches, and provides guidelines on how to wrap the printed object via small curves illustrating the patch boundaries etched on the printed object as well as an animation showing how the 2D patches should be folded together. To avoid wasting materials, a heuristics method is also employed to pack 2D patches in the layout. To compensate the effect of inflation resulted from covering objects with thick materials, an offsetting tool is provided in Cover-it. In addition, since many low scale details of an object is not visible after covering, a mesh can be simplified in Cover-it to reduce the number of 2D patches.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.278
Teacher spread0.223 · 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