MétaCan
Menu
Back to cohort
Record W3084063516 · doi:10.1364/oe.402310

Camera-free three-dimensional dual photography

2020· article· en· W3084063516 on OpenAlexafffund
Patrick Kilcullen, Cheng Jiang, T. Ozaki, Jinyang Liang

Bibliographic record

VenueOptics Express · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsPhotographyComputer scienceComputational photographyComputer visionOpticsComputer graphics (images)Artificial intelligenceDigital micromirror deviceStructured-light 3D scannerImage processingPhysicsImage (mathematics)

Abstract

fetched live from OpenAlex

We report camera-free three-dimensional (3D) dual photography. Inspired by the linkage between fringe projection profilometry (FPP) and dual photography, we propose to implement coordinate mapping to simultaneously sense the direct component of the light transport matrix and the surface profiles of 3D objects. By exploiting Helmholtz reciprocity, dual photography and scene relighting can thus be performed on 3D images. To verify the proposed imaging method, we have developed a single-pixel imaging system based on two digital micromirror devices (DMDs). Binary cyclic S-matrix patterns and binary sinusoidal fringe patterns are loaded on each DMD for scene encoding and virtual fringe projection, respectively. Using this system, we have demonstrated viewing and relighting 3D images at user-selectable perspectives. Our work extends the conceptual scope and the imaging capability of dual photography.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.235
Teacher spread0.218 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueOptics ExpressSame topicAdvanced Optical Sensing TechnologiesFrench-language works237,207