Diffuse shading, visibility fields, and the geometry of ambient light
Bibliographic record
Abstract
A dominating sky principle is introduced. It states that under diffuse lighting conditions, ambient light has a bimodal structure: the sky is typically much brighter than the ground. The dominating sky principle suggests a general model of image formation under diffuse lighting conditions. The idea behind this model is that the luminous intensity of a surface is determined by the visible region of the diffuse source, or surface visibility function. The model is presented. A novel concept in shading analysis, surface aperture, is discussed. Surface aperture is the solid angle of the visible diffuse source. Although the unit surface normal is a factor under diffuse lighting conditions, in most situations surface aperture dominates. The surface visibility and aperture functions are embedded into visibility and aperture fields defined in the free space above the surfaces. The visibility field is useful because it satisfies simple local constraints. A fast, parallel computation is described for the forward problem of rendering a surface which is illuminated by a diffuse source and for the inverse problem, namely, shape from shading on a cloudy day. The forward and inverse solutions are quite similar. Experimental results are presented.>
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".