Luminance Calibration of High Dynamic Range Panoramas Using a Regular Tetrahedron Illuminance Meter: Part 1 – Theory and Simulations
Bibliographic record
Abstract
High dynamic range (HDR) panoramic images have been taken as a new camera-aided lighting measurement technology. While the shooting of HDR panoramas has become quite easy with all kinds of commercially available software, the conventional luminance calibration procedure is complicated using a spot luminance meter and standard gray cards. This article proposes a solution to calibrate the luminance of HDR panoramas based on the physical measurement of scalar illuminance by a regular tetrahedron illuminance meter. The theoretical explanation for how the scalar illuminance can be approximated by the average illumination in the four directions of a regular tetrahedron’s faces was given. The performance of a cubic illuminance meter and a tetrahedron illuminance meter was tested under 205 indoor and 2,233 outdoor panoramas from the Laval HDR databases. The results indicate that the regular tetrahedron illuminance meter gave more reliable scalar illuminance, with an average absolute error being 1.7% and the relative standard deviation being 2%. As the scene gets more diffuse, the measurements get more robust. Then, the calibration factor is derived from the ratio of the measured scalar illuminance to the recovered scalar illuminance from the spherical harmonics decompositions of the HDR panoramas. This study has laid a theoretical basis for using an easily constructed regular tetrahedron illuminance meter to calibrate the luminance of HDR panoramas, which is much simpler to conduct, requires less time, has a lower cost, and is in acceptable precision. Furthermore, the regular tetrahedron illuminance meter has the potential to be integrated into a panoramic camera for the continuous calibration of fast-changing scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".