Target penetration of laser-based 3D imaging systems
Bibliographic record
Abstract
The ASTM E57.02 Test Methods Subcommittee is developing a test method to evaluate the ranging performance of a 3D imaging system. The test method will involve either measuring the distance between two targets or between an instrument and a target. The first option is necessary because some instruments cannot be centered over a point and will require registration of the instrument coordinate frame into the target coordinate frame. The disadvantage of this option is that registration will introduce an additional error into the measurements. The advantage of this option is that this type of measurement, relative measurement, is what is typically used in field applications. A potential target geometry suggested for the test method is a planar target. The ideal target material would be diffuse, have uniform reflectivity for wavelengths between 500 nm to 1600 nm (wavelengths of most commercially-available 3D imaging systems), and have minimal or no penetration of the laser into the material. A possible candidate material for the target is Spectralon1. However, several users have found that there is some penetration into the Spectralon by a laser and this is confirmed by the material manufacturer. The effect of this penetration on the range measurement is unknown. This paper will present an attempt to quantify the laser penetration depth into the Spectralon material for four 3D imaging systems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".