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Record W4366547439 · doi:10.1145/3544548.3580849

Perspective and Geometry Approaches to Mouse Cursor Control in Spatial Augmented Reality

2023· article· en· W4366547439 on OpenAlex
Daekun Kim, Nikhita Joshi, Daniel Vogel

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 institutionsUniversity of Waterloo
Fundersnot available
KeywordsCursor (databases)Computer scienceTraverseComputer graphics (images)Perspective (graphical)TracingComputer visionGeometryArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Spatial augmented reality (SAR) can extend desktop computing out of the monitor and into our surroundings, but extending the standard style of mouse input is challenging due to real-world geometry irregularity, gaps, and occlusion. We identify two general approaches for controlling a mouse cursor in SAR: perspective-based approaches based on raycasting, such as Nacenta et. al’s Perspective Cursor, and geometry-based approaches that closely associate cursor movement with surface topology. For the latter, we introduce Everywhere Cursor, a geometry-based approach for indirect mouse cursor control for complex 3D surface geometry in SAR. A controlled experiment compares approaches. Results show the geometry-based Everywhere Cursor improves accuracy and precision by 29% to 60% on average in a tracing task, but when traversing long distances, the perspective-based Perspective Cursor and Raycasting techniques are 22% to 49% faster, albeit with 4% to 10% higher error rates.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.287
Teacher spread0.192 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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