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Record W3175804713 · doi:10.1002/sdtp.14926

P‐49: <i>Student Poster:</i> Efficient Direct‐Block‐Address Encoding for Single‐Shot‐Based 3D Reconstruction

2021· article· en· W3175804713 on OpenAlexaff
Sina Farsangi, Mohamed A. Naiel, Mark Lamm, Paul Fieguth

Bibliographic record

VenueSID Symposium Digest of Technical Papers · 2021
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsChristie (Canada)University of Waterloo
Fundersnot available
KeywordsEncoding (memory)EmbeddingSingle shotComputer scienceProjectorBlock (permutation group theory)PixelComputer graphics (images)Shot (pellet)ComputationOne shotCode wordArtificial intelligenceComputer visionAlgorithmMathematicsDecoding methodsCombinatoricsOpticsPhysics

Abstract

fetched live from OpenAlex

We propose a new framework for single‐shot 3D reconstruction based on two‐dimensional tag embedding, where the Structured Light (SL) patterns are partitioned into a number of non‐overlapping blocks that each represent a codeword for explicitly encoding the projector's corresponding pixel address, resulting in an efficient computation for both constructing the SL images and for obtaining the projector‐camera pixel correspondences.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.273
Teacher spread0.242 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical · Other

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSID Symposium Digest of Technical PapersSame topicOptical measurement and interference techniquesFrench-language works237,207