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Sketching and Composing Widgets for 3D Manipulation

2008· article· en· W2131598794 on OpenAlexafffund
Ryan Schmidt, Karan Singh, Ravin Balakrishnan

Bibliographic record

VenueComputer Graphics Forum · 2008
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsComputer scienceSketchHuman–computer interactionTransformation (genetics)Object (grammar)Context (archaeology)Interface (matter)VocabularyUser interfaceComputer graphics (images)Programming languageArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

Abstract We present an interface for 3D object manipulation in which standard transformation tools are replaced with transient 3D widgets invoked by sketching context‐dependent strokes. The widgets are automatically aligned to axes and planes determined by the user's stroke. Sketched pivot‐points further expand the interaction vocabulary. Using gestural commands, these basic elements can be assembled into dynamic, user‐constructed 3D transformation systems. We supplement precise widget interaction with techniques for coarse object positioning and snapping. Our approach, which is implemented within a broader sketch‐based modeling system, also integrates an underlying “widget history” to enable the fluid transfer of widgets between objects. An evaluation indicates that users familiar with 3D manipulation concepts can be taught how to efficiently use our system in under an hour.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.253
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations63
Published2008
Admission routes2
Has abstractyes

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