Chiaroscuro Photogrammetry: Revolutionizing 3D Modeling in Low Light Conditions for Archaeological Sites
Bibliographic record
Abstract
Archaeologists working in low light conditions have had difficulty producing 3D models that are both scientific and aesthetic. We are presenting chiaroscuro photogrammetry, a technique inspired by Renaissance artists, to solve this problem. The method is portable, inexpensive, low impact, adaptable, fast, and requires no additional expertise beyond photogrammetry. While first trialed on a rock and a tree that produced promising outcomes, the true test was on a panel of finger flutings in a completely dark chamber of Koonalda Cave, South Australia. The result was a 3D model of the finger flutings with evenly balanced light and deep colors, and the geometry of the model was free from holes and visible artifacts. The 3D model produced using chiaroscuro photogrammetry was visually and geometrically accurate, even more so than flash photogrammetry. Chiaroscuro photogrammetry has the potential to revolutionize 3D modeling in low light conditions for a variety of archaeological contexts.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".