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A practical method for realistic simulation of non-point light sources in commonly used computer graphics softwares

2024· article· en· W4399729044 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsComputer scienceComputer graphicsComputer graphics (images)GraphicsPoint (geometry)Computational scienceMathematics

Abstract

fetched live from OpenAlex

Simulation of light sources using photometric data can result in realistic scenes which is necessary in many applications such as planning autonomous visual inspection. However, most computer graphics softwares like Blender are not capable of doing simulations for luminaires that are non-point sources, like bar or ring lights. Since a wide variety of non-point lights are being used in the real world, filling the existing gap, and simulating them is a valuable step. In the present paper, a method is presented to model the light texture of a bar light using multiple point lights. The proposed method is evaluated in DIALux which is a lighting software with accurate light calculations. By utilizing multiple sets of point lights in the simulations, the proper number of point lights for the luminaire based on the application requirements is studied. After showing the feasibility, the method is implemented in Blender software and the simulation results are compared with DIALux software to confirm the applicability of the method in Blender. Moreover, a rough light calculation method based on exposure values and false color representation is studied for Blender software and evaluated with results of DIALux which can result in a useful index for some applications.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.048
GPT teacher head0.403
Teacher spread0.355 · 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