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Record W4313306394 · doi:10.1109/mcg.2022.3230644

Mobile Augmented Reality for Adding Detailed Multimedia Content to Historical Physicalizations

2022· article· en· W4313306394 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Computer Graphics and Applications · 2022
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsAugmented realityComputer scienceVisualizationComputer graphics (images)OverlayMultimediaMobile deviceTracking (education)Computer graphicsData visualizationHuman–computer interactionArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

Combining augmented reality (AR) and physicalization offers both opportunities and challenges when representing detailed historical data. In this article, we describe a framework where mobile AR supplements views of 3-D prints of historical locations with interactive functionality and small visual details that the prints alone cannot display. Since seeing certain details requires bringing the camera close to the physical objects, the resulting camera frames may lack the visual information necessary to determine objects' positions and accurately superimpose the overlay. We address this by enhancing tracking of 3-D prints at close distances and employing visualization techniques that allow viewing small details in ways that do not interfere with tracking. To demonstrate these techniques, we apply our framework to the preservation of two heritage sites that represent large real-life areas containing smaller details of interest.

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: Methods · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.961

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.281
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