Visualization of Frescoed Surfaces: Buonconsiglio Castle - Aquila Tower, Cycle of the Months
Bibliographic record
Abstract
One of the major masterpieces of international Gothic art (1350-1450) is located in the Aquila tower in Buonconsiglio castle, Trento, Italy. The main room in the tower is completely frescoed with the <em>Cycle of the Months</em>, a rare example of medieval painting on a nonreligious subject. The frescos depict medieval time month by month with stunning details of the landscape, costumes, and every element of daily life. As part of the PEACH (Personal Experience with Active Cultural Heritage) project, the objective of this work is to acquire the most suitable data and generate a textured 3D model for interactive manipulation and creation of a photo-realistic walkthrough movie. This will give a vista to scientists, conservationists, historians, and visitors for looking at and studying this room and its frescos in virtual reality. Since photo-realism of the frescos is of utmost importance, it became apparent that many issues related to geometric and radiometric distortions must be addressed. For example, texture data is typically collected as images containing specific lighting conditions. When these images are stitched together, discontinuities are usually visible. Another predicament is the real-time requirement of visualization and manipulation of 3D models. Since it is important to maintain the best texture quality to visualize the details of the frescos and at the same time have smooth interactive visualization, memory problems had to be addressed. We build upon existing techniques developed for texture acquisition and reconstruction to generate efficient maps of high visual quality. Results of the first phase of the project are presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".