Surface reconstruction of large complex structures from mixed range data – The Erechtheion experience
Bibliographic record
Abstract
We present the procedure and technologies used to modelling of the Erechtheion at the Acropolis in Athens, a large complex structure with many challenges to data acquisition, processing, and visualisation. We used two scanners, one for medium range highresolution coverage (1-5 mm lateral data spacing) and one long-range scanner to capture some top sections occluded from the first scanner. We also used high-resolution digital images for image-based reconstruction using Photogrammetric and image matching techniques and for texture mapping. We focus in this paper on the geometric reconstruction from the range sensors. Several issues had to be addressed. Our system must be able to work with data obtained at different resolutions and accuracies and acquired from any viewpoint. The marble surfaces were of different age from one part to another due to decades of restoration. This resulted in different reflectance properties and apparent laser penetration that varied from area to area. Another problem resulted from the huge amount of data. The billions of generated 3D points by all sensors are currently beyond the processing capabilities of commercially available software or hardware. Reducing the data to a manageable size without losing important details had to be addressed. 1.
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How this classification was reachedexpand
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) | 0.006 | 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 teacher head, 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".