NRC's 3D technology for museum and heritage applications
Bibliographic record
Abstract
For archiving, art historical purposes and restoration, "two dimensional" (2-D) works of art such as paintings or photo-graphs have, in modern times, traditionally been documented using (colour) photography and/or digital imaging tech-niques. While current technology allows reproductions with high spatial and colour resolution, these techniques only document the 2-D form and colour of the surface of the object. Surfaces also have, however, three dimensional (3-D) properties which play just as important a role in determining an objects appearance and how it is perceived. Among these, surface roughness/texture and the optical properties of transparent layers are particularly important. Both determine the way light is scattered from the surface, and influence not only colour perception, but also glossiness, illusions of transparency, and depth perception. These properties are often the first to be affected before colour changes happen or are perceived in a measurable quantity. They are difficult, if not, impossible to document using current 2-D techniques, while current 3-D scanners do not have the resolution necessary to document the micro-roughness of surfaces which actually determine appearance. The Netherlands Institute of Cultural Heritage has started a multidisciplinary programme to study the effect of 3-D pro-perties of surfaces on the appearance of works of art. The combined effects of colour, roughness, and optical properties of transparent layers are being investigated in situ using standard engineering micro-roughness measurements, colour spectroscopy, and digital imaging techniques. Initial work indicates that these are excellent methods for documenting, for example, the effects of cleaning of paintings and face-mounted photographs. Combined with light scattering models, they will provide a good tool for understanding the appearance of objects, and for their conservation. Rendering models, for example, could help conservators make selections of retouching materials based on colour and texture, or varnishes based on optical properties.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.075 |
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".