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Record W2008755494 · doi:10.1155/2010/419493

Stereo 3D Mouse Cursor: A Method for Interaction with 3D Objects in a Stereoscopic Virtual 3D Space

2009· article· en· W2008755494 on OpenAlexafffund
Hossein Azari, Irene Cheng, Anup Basu

Bibliographic record

VenueInternational Journal of Digital Multimedia Broadcasting · 2009
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsAthabasca UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaClemson University
KeywordsCursor (databases)Computer scienceStereoscopyRendering (computer graphics)Computer graphics (images)VisualizationComputer visionArtificial intelligenceHuman–computer interaction

Abstract

fetched live from OpenAlex

We introduce a different approach of applying stereoscopy principles to implement a virtual 3D pointing technique called stereo 3D mouse cursor (S3D-Cursor) based on two or more views of an ordinary mouse cursor. The basics of such an idea have already been applied as a by-product of some stereo-based visualization applications with usually less attention to its strengths or weaknesses as a generic alternative of its 2D counterpart in stereoscopic 3D space. Here, we examine if such an idea satisfies all or the main expected requirements of an abstract 3D cursor. Moreover, we analyze its accuracy and evaluate the applicability of this approach in terms of different efficiency factors. For this purpose, we have adapted a real-time point-based rendering software called QSplat to a multiview rendering version named QSplatMV. We have implemented the S3D-Cursor on top of this new application and developed a simple editing toolset for manipulating the virtual 3D objects. Our user evaluation results suggest the effectiveness of the approach in terms of detection accuracy and user satisfaction compared to using an ordinary mouse cursor on a conventional 2D screen.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.990
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.000
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 head0.020
GPT teacher head0.334
Teacher spread0.314 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2009
Admission routes2
Has abstractyes

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