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Record W2604816576 · doi:10.1109/vr.2017.7892376

3DPS: An auto-calibrated three-dimensional perspective-corrected spherical display

2017· article· en· W2604816576 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of SaskatchewanUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer graphics (images)Computer visionRendering (computer graphics)ProjectorParallaxArtificial intelligenceVisualizationAugmented realityPerspective (graphical)PixelStereo display

Abstract

fetched live from OpenAlex

We describe an auto-calibrated 3D perspective-corrected spherical display that uses multiple rear projected pico-projectors. The display system is auto-calibrated via 3D reconstruction of each projected pixel on the display using a single inexpensive camera. With the automatic calibration, the multiple-projector system supports a seamless blended imagery on the spherical screen. Furthermore, we incorporate head tracking with the display to present 3D content with motion parallax by rendering perspective-corrected images based on the viewpoint. To show the effectiveness of this design, we implemented a view-dependent application that allows walk-around visualization from all angles for a single head-tracked user. We also implemented a view-independent application that supports a wall-papered rendering for multi-user viewing. Thus, both view-dependent 3D VR content and spherical 2D content, such as a globe, can be easily experienced with this display.

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 score0.953
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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