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Record W1561079676

Object representation using 1D displacement mapping

2004· article· en· W1561079676 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

VenueGraphics Interface · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRendering (computer graphics)Displacement mappingComputer visionComputer scienceArtificial intelligenceComputer graphics (images)Graphics hardwareFrame rateTexture mappingTiled renderingDisplacement (psychology)Software renderingTerrain renderingImage-based modeling and rendering3D renderingPreprocessorProjective texture mappingGraphicsObject (grammar)Image textureImage (mathematics)Image processing3D computer graphics
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we propose a new method for rendering image-based objects. An object is decomposed into a set of 1D displacement textures in a preprocessing stage. During rendering, each of these 1D displacement textures is rendered to its appropriate position in 3D space, using the hardware accelerated displacement mapping approach. Our method can represent and render complex objects with accurate appearance from arbitrary viewpoints. It can also render a complex object at a high frame rate, using commodity graphics hardware. The 1D displacement texture set is reconstructed from the images with depth maps.

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 score0.798
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.348
Teacher spread0.296 · 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