Improvement in measurement accuracy for hybrid scanner
Bibliographic record
Abstract
The capability to provide dense three-dimensional (3D) data (point clouds) at high speed and at high accuracy has made terrestrial laser scanners (TLS) widely used for many \npurposes especially for documentation, management and analysis. However, similar to other 3D sensors, proper understanding regarding the error sources is necessary to ensure high quality data. A procedure known as calibration is employed to evaluate these errors. This process is crucial for TLS in order to make it suitable for accurate 3D applications (e.g. industrial measurement, reverse engineering and monitoring). Two calibration procedures \navailable for TLS: 1) component, and 2) system calibration. The requirements of special laboratories and tools which are not affordable by most TLS users have become principle \ndrawback for component calibration. In contrast, system calibration only requires a room with appropriate targets. By employing optimal network configuration, this study has performed system calibration through self-calibration for Leica ScanStation C10 scanner. A laboratory with dimensions of 15.5m x 9m x 3m and 138 well-distributed planar targets were used to derive four calibration parameters. Statistical analysis (e.g. t-test) has shown that only two \ncalculated parameters, the constant rangefinder offset error (0.7mm) and the vertical circle index error (-45.4inch were significant for the calibrated scanner. Photogrammetric technique was utilised to calibrate the 3D test points at the calibration field. By using the test points, the residual pattern of raw data and self-calibration results were plotted into the graph to visually demonstrate the improvement in accuracy for Leica ScanStation C10 scanner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".