Accuracy Verification and Enhancement in 3D Modeling: Application to Donatello's Maddelena
Bibliographic record
Abstract
The three-dimensional acquisition and modeling of Donatello's Maddalena was started in order to create a methodology that aims at monitoring fragile wooden sculptures over the years. Hence a set of new approaches in 3D modeling was needed for obtaining the necessary metric reliability. The main focus of the work is modeling accuracy, therefore quality control procedures based both of the self check of 3D data and the use of complementary 3D sensors were developed for testing the model. Sensor fusion was also extensively used in order to correct a few alignment errors after the ICP phase leading to a not negligible overall metric discrepancy. All the steps of the acquisition procedure, from the project planning to the solution of the various technical and logistical problems are reported. Although few commercial systems claim to use a similar approach, for the first time, the non-invasive integration of photogrammetry and 3D scanning, specifically designed for applications in Cultural Heritage, is here extensively documented.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".