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Record W4308990731 · doi:10.1145/3567713

Conductor: Intersection-Based Bimanual Pointing in Augmented and Virtual Reality

2022· article· en· W4308990731 on OpenAlexaff
Futian Zhang, Keiko Katsuragawa, Edward Lank

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2022
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConductorComputer scienceLeverage (statistics)Intersection (aeronautics)Virtual realityHuman–computer interactionCursor (databases)Selection (genetic algorithm)Computer visionArtificial intelligenceEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Pointing is an elementary interaction in virtual and augmented reality environments, and, to effectively support selection, techniques must deal with the challenges of occlusion and depth specification. Most of the previous techniques require two explicit steps to handle occlusion. In this paper, we propose Conductor, an intuitive, plane-ray, intersection-based, 3D pointing technique where users leverage bimanual input to control a ray and intersecting plane. Conductor allows users to use the non-dominant hand to adjust the cursor distance on the ray while pointing with the dominant hand. We evaluate Conductor against Raycursor, a state-of-the-art VR pointing technique, and show that Conductor outperforms Raycursor for selection tasks. Given our results, we argue that bimanual selection techniques merit additional exploration to support object selection and placement within virtual environments.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.048
GPT teacher head0.315
Teacher spread0.268 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2022
Admission routes1
Has abstractyes

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