High-Accuracy 3-D Modeling of Cultural Heritage: The Digitizing of Donatello's “Maddalena”
Bibliographic record
Abstract
Three-dimensional digital modeling of Heritage works of art through optical scanners, has been demonstrated in recent years with results of exceptional interest. However, the routine application of three-dimensional (3-D) modeling to Heritage conservation still requires the systematic investigation of a number of technical problems. In this paper, the acquisition process of the 3-D digital model of the Maddalena by Donatello, a wooden statue representing one of the major masterpieces of the Italian Renaissance which was swept away by the Florence flood of 1966 and successively restored, is described. The paper reports all the steps of the acquisition procedure, from the project planning to the solution of the various problems due to range camera calibration and to material non optically cooperative. Since the scientific focus is centered on the 3-D model overall dimensional accuracy, a methodology for its quality control is described. Such control has demonstrated how, in some situations, the ICP-based alignment can lead to incorrect results. To circumvent this difficulty we propose an alignment technique based on the fusion of ICP with close-range digital photogrammetry and a non-invasive procedure in order to generate a final accurate model. In the end detailed results are presented, demonstrating the improvement of the final model, and how the proposed sensor fusion ensure a pre-specified level of accuracy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".