A multi‐illuminant synthetic image test set
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.
Bibliographic record
Abstract
Abstract A new multi‐illuminant synthetic image test set called MIST is described and made publicly available. MIST is intended primarily for evaluating illumination estimation and color constancy methods, but additional data are provided to make it useful for other computer vision applications as well. MIST addresses the problem found in most existing real‐image datasets, which is that the groundtruth illumination is only measured at a very limited number of locations, despite the fact that illumination tends to vary significantly in almost all scenes. In contrast, MIST provides for each pixel: (a) the percent surface spectral reflectance, (b) the spectrum of the incident illumination, (c) the separate specular and diffuse components of the reflected light, and (d) the depth (ie, camera‐to‐surface distance). The dataset contains 900 stereo pairs, each of the 1800 images being a 30‐band multispectral image covering the visible spectrum from 400 to 695 nm at a 5 nm interval. Standard sRGB versions of the multispectral images are also provided. The images are synthesized by extending the Blender Cycles ray‐tracing renderer. The rendering is done in a way that ensures the images are not only photorealistic, but physically accurate as well.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it