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Record W2035126578 · doi:10.1145/1242073.1242278

The spatial bi-directional reflectance distribution function

2002· article· en· W2035126578 on OpenAlexaff
David McAllister, Anselmo Lastra, Benjamin P. Cloward, Wolfgang Heidrich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBidirectional reflectance distribution functionPixelRepresentation (politics)Computer scienceReflectivityArtificial intelligencePoint (geometry)Spatial distributionCurse of dimensionalityComputer visionTexture (cosmology)Function (biology)Bidirectional texture functionSurface (topology)Set (abstract data type)NormalDirectional derivativeOpticsImage textureImage (mathematics)Remote sensingMathematicsGeologyGeometryImage processingPhysics

Abstract

fetched live from OpenAlex

Combining texture mapping with bi-directional reflectance distribution functions (BRDFs) yields a representation of surface appearance with both spatial and angular detail. We call a texture map with a unique BRDF at each pixel a spatial bi-directional reflectance distribution function, or SBRDF. The SBRDF is a six-dimensional function representing the reflectance from each incident direction to each exitant direction at each surface point. Because of the high dimensionality of the SBRDF, previous appearance capture and representation work has focused on either spatial or angular detail, has relied on a small set of basis BRDFs, or has only treated spatial detail statistically [Dana 1999; Lensch 2001].

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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 designTheoretical or conceptual
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

Citations21
Published2002
Admission routes1
Has abstractyes

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