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Record W2117843877 · doi:10.1145/2601097.2601144

A compressive light field projection system

2014· article· en· W2117843877 on OpenAlexfundno aff
Matthew Hirsch, Gordon Wetzstein, Ramesh Raskar

Bibliographic record

VenueACM Transactions on Graphics · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsnot available
FundersDivision of Information and Intelligent SystemsNatural Sciences and Engineering Research Council of Canada
KeywordsProjectorProjection (relational algebra)Light fieldComputer scienceComputer graphics (images)Field (mathematics)Compressed sensingStructured lightComputer visionComputational photographyOpticsArtificial intelligencePhysicsImage processingImage (mathematics)AlgorithmMathematics

Abstract

fetched live from OpenAlex

For about a century, researchers and experimentalists have strived to bring glasses-free 3D experiences to the big screen. Much progress has been made and light field projection systems are now commercially available. Unfortunately, available display systems usually employ dozens of devices making such setups costly, energy inefficient, and bulky. We present a compressive approach to light field synthesis with projection devices. For this purpose, we propose a novel, passive screen design that is inspired by angle-expanding Keplerian telescopes. Combined with high-speed light field projection and nonnegative light field factorization, we demonstrate that compressive light field projection is possible with a single device. We build a prototype light field projector and angle-expanding screen from scratch, evaluate the system in simulation, present a variety of results, and demonstrate that the projector can alternatively achieve super-resolved and high dynamic range 2D image display when used with a conventional screen.

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)0.001
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations79
Published2014
Admission routes1
Has abstractyes

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