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Record W2103272296 · doi:10.1109/icpr.2000.905502

Generic modeling of 3D objects from single 2D images

2002· article· en· W2103272296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceA priori and a posterioriComputer visionArtificial intelligenceObject (grammar)Set (abstract data type)Context (archaeology)Basis (linear algebra)Solid modeling3D modeling3d modelContext modelComputer graphics (images)MathematicsGeometry

Abstract

fetched live from OpenAlex

Addresses the problem of building generic 3D models of structured objects on the basis of single 2D intensity images. In the context of the paper, generic modeling refers to the situation where analysis of the image information is performed on the sole basis of generic knowledge. That is, no a priori knowledge about the specific quantitative shape properties of the objects of interest is ever assumed. Moreover, images of interest are realistic. For instance, they may contain complex foreground 3D objects with textures and shadows, and a cluttered background. Objects are modeled by their constituent parts and connections. Therefore, a partly occluded object could be recognized from its model. Part models are based on geons, which are a set of qualitative generalized cylinders. An overview of the architecture of the modeling system is presented, along with the functionality of each subsystem and processing results.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.233

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 science0.0000.000
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 head0.030
GPT teacher head0.208
Teacher spread0.178 · 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

Quick stats

Citations6
Published2002
Admission routes2
Has abstractyes

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