Detailed 3D Reconstruction of Monuments Using Multiple Techniques
Bibliographic record
Abstract
The use of 3D digitization and modeling in documenting heritage sites has increased significantly over the past few years. This is mainly due to advances in laser scanning techniques, 3D modeling software, image-based-modeling techniques, computer power, and virtual reality. There are many approaches currently available. The most common remains based on surveying and CAD tools and/or traditional photogrammetry with control points and a human operator. This is very time consuming and can be tedious and sustained effort. Lately, modeling methods based on scanners data and more automated image-based technique are becoming available. We will discuss each approach and point out its advantages and disadvantages. We will then present our approach, which is a combination of several technologies. The approach presented in this paper uses both interactive and automatic techniques, each where it is best suited, to accurately and completely model heritage objects and sites. A highly detailed structure or site can be modeled at various levels of detail. Image-based modeling may be used for the basic shape and main structural elements, and laser scanning for fine details and sculpted surfaces. The results of applying this approach were very encouraging and several models were created from sites all over the world. Modeling of the Abbey of Pomposa near Ferrara, Italy, will be presented as an example.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".