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Record W4414342645 · doi:10.1111/cgf.70219

Spherical Harmonic Exponentials for Efficient Glossy Reflections

2025· article· en· W4414342645 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

VenueComputer Graphics Forum · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsMila - Quebec Artificial Intelligence InstituteMcGill UniversityCanadian Institute for Advanced Research
Fundersnot available
KeywordsSpecular reflectionExponential functionSpherical harmonicsRepresentation (politics)HarmonicExploitIterative reconstructionQuality (philosophy)

Abstract

fetched live from OpenAlex

Abstract We propose a high‐performance and compact method for computing glossy specular reflections. Commonly‐used prefiltered environment maps have large storage requirements and high error due to constrained treatment of view‐dependence. We propose a factorized spherical harmonic exponential representation that exploits new observations of the benefits of log‐space reconstruction for reflectance. Our method is compact, properly accounts for view‐dependent reflections, and is more accurate than the state‐of‐the‐industry solutions. We achieve higher quality results with an order of magnitude less memory, all with efficient and alias‐free reconstruction of glossy reflections from environment lights and continuously‐varying material roughness.

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)
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 score0.826
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
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.029
GPT teacher head0.334
Teacher spread0.305 · 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