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Record W2060034473 · doi:10.1145/1809939.1809958

Towards mapping the field of non-photorealistic rendering

2010· article· en· W2060034473 on OpenAlexafffund
Amy A. Gooch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Victoria
FundersUniversity of VictoriaNatural Sciences and Engineering Research Council of CanadaNorthwestern University
KeywordsRendering (computer graphics)Computer graphics (images)Computer scienceImage-based modeling and renderingNon-photorealistic renderingComputer visionTexture mappingArtificial intelligenceAnimationComputer animationComputer facial animation

Abstract

fetched live from OpenAlex

Non-photorealistic rendering (NPR) as a field of research is generally described by what it is not and as a result it is often hard to embrace the strengths of non-photorealistic rendering as a discipline beyond digitizing or replicating traditional artistic techniques. Towards generating more discussion within the discipline, this paper provides a simple theory of NPR as a way of mapping perceived changes in a scene to perceived changes in a display. One can think of a photorealistic image as one that preserves a one-to-one mapping, such that parameters such as color, intensity, texture, edges, etc. in a scene are mapped to the same parameters in the display. NPR mappings are not one-to-one. For example edges in a scene may be mapped to black lines to generate a cartoon effect. Within this framework of mappings, a partial listing of previous techniques within the discipline is provided. The aim of this paper is to provide a type of road map to stimulate the future growth of the area of non-photorealistic rendering.

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 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: none
Teacher disagreement score0.834
Threshold uncertainty score0.122

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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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

Citations5
Published2010
Admission routes2
Has abstractyes

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