Exploring a Byzantine Crypt Through a High-Resolution Texture Mapped 3D Model: Combining Range Data and Photogrammetry
Bibliographic record
Abstract
In recent years, high-resolution recording of heritage sites has stimulated a lot of research in fields like photogrammetry, computer vision, and computer graphics. Numerous algorithms and methodologies have been proposed in the literature. In practice, what a 3D photographer needs is a commercially available solution to this so-called as-built documentation. In this paper, we present an effective approach for photo-realistic 3D model building from the combination of photogrammetry and 3D range data. The method is applied to the virtualization of a Byzantine Crypt where geometrically correct texture mapping is essential to render the environment accurately in order to produce enticing virtual visits, apply virtual restoration techniques on the frescoes and remove architectural elements that have been added over the years so that the site can then be viewed in the correct historical context. A movie entitled "CARPINIANA" was created in order to demonstrate the results.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".