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Record W2086007364 · doi:10.1117/12.2004537

S3D depth-axis interaction for video games: performance and engagement

2013· article· en· W2086007364 on OpenAlexaff
Chris Zerebecki, Brodie Stanfield, Andrew Hogue, Bill Kapralos, Karen Collins

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of WaterlooOntario Tech University
Fundersnot available
KeywordsStereoscopyAffordanceComputer scienceHuman–computer interactionPerceptionProcess (computing)Mode (computer interface)Stereo displayDepth perceptionMultimediaComputer graphics (images)Computer visionPsychology

Abstract

fetched live from OpenAlex

Game developers have yet to embrace and explore the interactive stereoscopic 3D medium. They typically view stereoscopy as a separate <i>mode</i> that can be disabled throughout the design process and rarely develop game mechanics that take advantage of the stereoscopic 3D medium. What if we designed games to be S3D-specific and viewed traditional 2D viewing as a separate mode that can be disabled? The design choices made throughout such a process may yield interesting and compelling results. Furthermore, we believe that interaction within a stereoscopic 3D environment is more important than the visual experience itself and therefore, further exploration is needed to take into account the interactive affordances presented by stereoscopic 3D displays. Stereoscopic 3D displays allow players to perceive objects at different depths, thus we hypothesize that designing a core mechanic to take advantage of this viewing paradigm will create compelling content. In this paper, we describe <i>Z-Fighter</i> a game that we have developed that requires the player to interact directly along the stereoscopic 3D depth axis. We also outline an experiment conducted to investigate the performance, perception, and enjoyment of this game in stereoscopic 3D vs. traditional 2D viewing.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicVirtual Reality Applications and ImpactsFrench-language works237,207