A practical method for realistic simulation of non-point light sources in commonly used computer graphics softwares
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".