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Record W4229457588 · doi:10.1364/oe.457502

Multi-scale band-limited illumination profilometry for robust three-dimensional surface imaging at video rate

2022· article· en· W4229457588 on OpenAlexafffund
Cheng Jiang, Patrick Kilcullen, Yingming Lai, Siqi Wang, T. Ozaki, Jinyang Liang

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversité du Québec
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilCanada Foundation for Innovation
KeywordsProfilometerOpticsRobustness (evolution)Structured-light 3D scannerProjection (relational algebra)Computer scienceStructured lightComputer visionDigital Light ProcessingArtificial intelligenceMaterials scienceScannerPhysicsSurface roughness

Abstract

fetched live from OpenAlex

Dynamic three-dimensional (3D) surface imaging by phase-shifting fringe projection profilometry has been widely implemented in diverse applications. However, existing techniques fall short in simultaneously providing the robustness in solving spatially isolated 3D objects, the tolerance of large variation in surface reflectance, and the flexibility of tunable working distances with meter-square-level fields of view (FOVs) at video rate. In this work, we overcome these limitations by developing multi-scale band-limited illumination profilometry (MS-BLIP). Supported by the synergy of dual-level intensity projection, multi-frequency fringe projection, and an iterative method for distortion compensation, MS-BLIP can accurately discern spatially separated 3D objects with highly varying reflectance. MS-BLIP is demonstrated by dynamic 3D imaging of a translating engineered box and a rotating vase. With an FOV of up to 1.7 m × 1.1 m and a working distance of up to 2.8 m, MS-BLIP is applied to capturing full human-body movements at video rate.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.049
GPT teacher head0.270
Teacher spread0.222 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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