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Record W1999241106 · doi:10.1002/jsid.285

Wide field of view compressive light field display using a multilayer architecture and tracked viewers

2014· article· en· W1999241106 on OpenAlexfundno aff
Renjie Chen, Andrew Maimone, Henry Fuchs, Ramesh Raskar, Gordon Wetzstein

Bibliographic record

VenueJournal of the Society for Information Display · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation SingaporeMitsubishi Electric Research LaboratoriesNational Science FoundationUniversity of North Carolina at Chapel HillSamsung
KeywordsComputer scienceLight fieldField of viewField (mathematics)Computer graphics (images)Computer visionSoftwareDepth of fieldArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this paper, we discuss an intuitive extension to compressive multilayer light field displays that greatly extends their field of view and depth of field. Rather than optimizing these displays to create a moderately narrow field of view at the center of the display, we constrain optimization to create narrow view cones that are directed to a few viewers' eyes, allowing the available display bandwidth to be utilized more efficiently. These narrow view cones follow the viewers, creating a wide apparent field of view. Imagery is also recalculated for the viewers' exact eye positions, creating a greater depth of field. The view cones can be scaled to match the positional error and latency of the tracking system. Using more efficient optimization and commodity tracking hardware and software, we demonstrate a real‐time, glasses‐free 3D display that offers a 100° × 40° field of view.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of the Society for Information DisplaySame topicAdvanced Optical Imaging TechnologiesFrench-language works237,207