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Record W3036572276 · doi:10.1145/3391614.3393651

Compensating for Perspective-based Distortion on Large Interactive Floor Displays: the SpaceHopper Field Experiment

2020· article· en· W3036572276 on OpenAlexaff
Juliano Franz, Joseph Malloch, Derek Reilly

Bibliographic record

VenueACM International Conference on Interactive Media Experiences · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPerspective (graphical)Image warpingComputer sciencePerspective distortionInteractorHuman–computer interactionField (mathematics)Computer visionArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Very large floor displays can promote engaging public experiences, but incur perspective-related warping, making it challenging to comprehend and interact with distal objects when standing on the display. We introduce a perspective compensated view technique that maintains the relative size and shape of objects as they move away from the viewer, and explore the technique in SpaceHopper, a large-scale, floor-projected version of the game Asteroids. Players bounce on a hopper ball to control their ship in one of two control modes: bounce to repel or bounce to shoot. We evaluated SpaceHopper in a field experiment with 59 participants, finding that perspective compensated view yielded longer playing times (and higher scores) in the bounce to fire modality. Bystanders were highly engaged with players and also seemed to be unaware of the perspective warping, suggesting that visually compensating for the interactor’s perspective does not adversely impact the enjoyment of passive participants and audience members, at least under some circumstances.

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)0.004
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 score0.767
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0020.000
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 head0.100
GPT teacher head0.379
Teacher spread0.280 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

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