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Record W2156435551 · doi:10.1109/pccga.2003.1238253

Interactive 3D model acquisition and registration

2004· article· en· W2156435551 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceUndersamplingProcess (computing)Computer visionArtificial intelligenceRepresentation (politics)Geometric modelingFrame (networking)Image registrationCoordinate systemComputer graphics (images)Image (mathematics)Mathematics

Abstract

fetched live from OpenAlex

The acquisition of geometric information from real-world objects has become a major way of modeling complex scenes and environments. Unfortunately, most optical methods for geometric model acquisition require the combination of partial information from different view points in order to obtain a single, coherent model. This, in turn, requires the registration of partial models into a common coordinate frame, a process that is usually done offline. As a consequence, holes due to undersampling and missing information often cannot be detected until after the registration. In this paper we introduce a fast, hardware-accelerated method for registering a new view to an existing partial geometric model in a volumetric representation. This method currently performs roughly one registration every second, and is therefore fast enough for on-the-fly evaluation by the user. Given more time, the same method is also capable of producing full geometric models at very high quality.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.188

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 communication0.0000.001
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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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