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Record W2982296756 · doi:10.1109/3dv.2019.00030

Frequency Shift Triangulation: A Robust Fringe Projection Technique for 3D Shape Acquisition in the Presence of Strong Interreflections

2019· article· en· W2982296756 on OpenAlexaff
Frank Billy Djupkep Dizeu, Jonathan Boisvert, Marc-Antoine Drouin, Guy Godin, Maxime Rivard, Guy Lamouche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsProjectorPixelComputer scienceStructured lightComputer visionArtificial intelligenceProjection (relational algebra)Image resolutionTriangulationEncoding (memory)Computer graphics (images)MathematicsAlgorithm

Abstract

fetched live from OpenAlex

We present the Frequency Shift Method, a new structured light technique allowing 3D shape acquisition in the presence of strong interreflections. The intensity signal of each camera pixel is represented by one or several peaks in the Fourier domain. If there is no interreflection, only a single peak appears, otherwise several peaks are present. Each peak represents a projector pixel participating in the illumination of the surface point imaged at the camera pixel. In the baseline version of the proposed approach, the number of patterns required is proportional to the number of projector pixels. We also propose a modification that significantly reduces the required number of patterns by subdividing and encoding the projector into multiple virtual low-resolution projectors. A method based on dynamic programming is used to separate direct and indirect illuminations. Our experimental results illustrate the effectiveness of our method compared to existing ones.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.069
GPT teacher head0.313
Teacher spread0.245 · 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
GenreMethods

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

Citations7
Published2019
Admission routes1
Has abstractyes

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