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Record W2795472713 · doi:10.1145/3173574.3173759

SymbiosisSketch

2018· article· en· W2795472713 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
TopicAugmented Reality Applications
Canadian institutionsAutodesk (Canada)University of Toronto
Fundersnot available
KeywordsSketchAffordanceComputer scienceAugmented realityHuman–computer interactionComputer graphics (images)3D interaction3d modelScale (ratio)Dual (grammatical number)Engineering drawingVirtual realityArtificial intelligenceEngineeringArtAlgorithm

Abstract

fetched live from OpenAlex

We present SymbiosisSketch, a hybrid sketching system that combines drawing in air (3D) and on a drawing surface (2D) to create detailed 3D designs of arbitrary scale in an augmented reality (AR) setting. SymbiosisSketch leverages the complementary affordances of 3D (immersive, unconstrained, life-sized) and 2D (precise, constrained, ergonomic) interactions for in situ 3D conceptual design. A defining aspect of our system is the ongoing creation of surfaces from unorganized collections of 3D curves. These surfaces serve a dual purpose: as 3D canvases to map strokes drawn on a 2D tablet, and as shape proxies to occlude the physical environment and hidden curves in a 3D sketch. SymbiosisSketch users draw interchangeably on a 2D tablet or in 3D within an ergonomically comfortable canonical volume, mapped to arbitrary scale in AR. Our evaluation study shows this hybrid technique to be easy to use in situ and effective in transcending the creative potential of either traditional sketching or drawing in air.

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 categoriesInsufficient payload (model declined to judge)
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 score0.949
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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

Quick stats

Citations185
Published2018
Admission routes1
Has abstractyes

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