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Record W1966039687 · doi:10.1142/s0219467806002288

RAYSET: A TAXONOMY FOR IMAGE-BASED RENDERING

2006· article· en· W1966039687 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

VenueInternational Journal of Image and Graphics · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of AlbertaLaurentian University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsRendering (computer graphics)Image warpingComputer scienceImage-based modeling and renderingArtificial intelligenceComputer visionTaxonomy (biology)Image-based lightingComputer graphics (images)

Abstract

fetched live from OpenAlex

A new concept, referred to as rayset, is discussed in this paper. It is a parametric function consisting of two mapping relations. The first one maps from a parameter space to the ray space, while the second one maps from the parameter space to the attribute space. A taxonomy is proposed based on the rayset concept whereby scene representations and scene reconstruction techniques used in image-based rendering are individually classified. Existing image-based rendering techniques are surveyed under the proposed classification. The review shows that different image-based scene representations, such as multiple-center-of-projection image and concentric mosaics, can be cast as different kinds of raysets. Different scene reconstruction approaches can be regarded as attempts to render different raysets. The concepts of rayset warping and rayset editing are also formulated. Under the rayset taxonomy, both techniques try to alter one of the mapping relations defined by a rayset without changing the other one.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.636
Threshold uncertainty score0.280

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 head0.284
Teacher spread0.266 · 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