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Record W4403108835 · doi:10.1111/cgf.15262

Dynamic Voxel‐Based Global Illumination

2024· article· en· W4403108835 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

VenueComputer Graphics Forum · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Regional Development FundMinisterio de Ciencia, Innovación y Universidades
KeywordsComputer scienceVoxelComputer graphics (images)Global illuminationComputer visionArtificial intelligenceRendering (computer graphics)

Abstract

fetched live from OpenAlex

Abstract Global illumination computation in real time has been an objective for Computer Graphics since its inception. Unfortunately, its implementation has challenged up to now the most advanced hardware and software solutions. We propose a real‐time voxel‐based global illumination solution for a single light bounce that handles static and dynamic objects with diffuse materials under a dynamic light source. The combination of ray tracing and voxelization on the GPU offers scalability and performance. Our divide‐and‐win approach, which ray traces separately static and dynamic objects, reduces the re‐computation load with updates of any number of dynamic objects. Our results demonstrate the effectiveness of our approach, allowing the real‐time display of global illumination effects, including colour bleeding and indirect shadows, for complex scenes containing millions of polygons.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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