3D Wide FOV Scanning Measurement System Based on Multiline Structured-Light Sensors
Bibliographic record
Abstract
Structured-light three-dimensional (3D) vision measurement is currently one of the most common approaches to obtain 3D surface data. However, the existing structured-light scanning measurement systems are primarily constructed on the basis of single sensor, which inevitably generates three obvious problems: limited measurement range, blind measurement area, and low scanning efficiency. To solve these problems, we developed a novel 3D wide FOV scanning measurement system which adopted two multiline structured-light sensors. Each sensor is composed of a digital CCD camera and three line-structured-light projectors. During the measurement process, the measured object is scanned by the two sensors from two different angles at a certain speed. Consequently, the measurement range is expanded and the blind measurement area is reduced. More importantly, since six light stripes are simultaneously projected on the object surface, the scanning efficiency is greatly improved. The Multiline Structured-light Sensors Scanning Measurement System (MSSS) is calibrated on site by a 2D pattern. The experimental results show that the RMS errors of the system for calibration and measurement are less than 0.092 mm and 0.168 mm, respectively, which proves that the MSSS is applicable for obtaining 3D object surface with high efficiency and accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".