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Record W2122096520 · doi:10.1109/3dui.2011.5759222

Pointing at 3D targets in a stereo head-tracked virtual environment

2011· article· en· W2122096520 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStylusComputer scienceFitts's lawComputer visionTask (project management)Artificial intelligenceStereo displayVirtual realityInput deviceComputer graphics (images)Computer hardwareEngineering

Abstract

fetched live from OpenAlex

We present three experiments that systematically examine pointing tasks in fish tank VR using the ISO 9241-9 standard. All experiments used a tracked stylus for a both direct touch and ray-based technique. Mouse-based techniques were also studied. Our goal was to investigate means of comparing 2D and 3D pointing techniques. The first experiment used a 2D task constrained to the display surface, allowing direct validation against other 2D studies. The second experiment used targets stereoscopically presented above and parallel to the display, i.e., the same task, but without tactile feedback afforded by the screen. The third experiment used targets varying in all three dimensions. Results of these studies suggest that the conventional 2D formulation of Fitts' law works well for planar pointing tasks even without tactile feedback, and with stereo display. Fully 3D motions using the ray and mouse based techniques are less well modeled.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.232
Teacher spread0.205 · 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

Citations151
Published2011
Admission routes2
Has abstractyes

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