Self-calibration of a structured light based scanner for use in archeological applications
Bibliographic record
Abstract
It is frequently necessary in archaeology to map excavated features so their structure can be recorded before they are dismantled in order for the excavation to continue. This process can be time consuming, error prone and manually intensive. Three-dimensional recording devices, which have the advantage of being faster, less labor intensive and more detailed, present an attractive alternative method of mapping. A small, portable hand scanner such as the DotProduct DPI-7, could be used for this purpose. However, the three-dimensional data collected from this device contain systematic distortions that cause errors in the recorded shape of the features being mapped. The performance of the DPI-7 scanner is evaluated in this paper using self-calibration based techniques. A calibration field consisting of spherical targets rigidly mounted on a planar background was imaged from multiple locations, and the target deviations from expected locations are used to quantify the performance of the device. The largest source of systematic error in the DPI-7 data was found to be a scale error affecting dimensions orthogonal to the depth. These in-plane distortions were modeled using a single scale factor parameter in the self-calibration solution, resulting in a 54% reduction in the RMS coordinate errors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".