Visualization of Frescoed Surfaces: Buonconsiglio Castle - Aquila Tower, Cycle of the Months
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
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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