Challenges in measuring spherical geometry using terrestrial laser scanners
Bibliographic record
Abstract
Terrestrial laser scanners (TLS) measure 3D coordinates in a scene by recording the range, the azimuth angle, and elevation angle of discrete points on target surfaces. They are increasingly used in a variety of applications, including manufacturing and civil infrastructure systems. However, the error sources of these instruments are not yet adequately characterized. There is a lack of standardized test procedures [1] and detailed uncertainty budgets for TLS measurements are seldom available. Measuring curved surfaces using TLS has always proved problematic; Lichti et. al [2] describe problems with cylinder measurements. From experiments performed at the laboratories of the Dimensional Metrology Group at NIST, we know that several TLS systems are incapable of obtaining a reliable point cloud from the surface of a spherical target. The changing surface curvature, averaging of the laser spot on the surface, multiple reflections from nearby surfaces and many other factors contribute to the scanned data of the sphere making it appear either smaller (squished) or larger (flared) than the actual sphere. This not only means that the radius of the sphere is incorrectly determined but there is also an error in locating the center of the sphere.In this context, we describe here the challenges involved in measuring a simple geometry, the sphere, using TLS
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".