A calibration method on 3D measurement based on structured-light with single camera
Bibliographic record
Abstract
The 3D shape measurement technology based on structured-light with a single camera has many advantageous aspects on usability, such as non-contact, high precision, high speed etc. There are various kinds of software accepting its measurement results readily. That is why it has been widely used in reality. System calibration is the key step before it begins normal scanning, and the setting of parameters in calibration directly affects the accuracy of the measurement. Some problems exist in the process of its calibration, such as the process is complicated and hard to operate, always taking low accuracy for the scanning result. This paper aims to find methods to solve the problems. The 3D scanning system used in the research is composed of a Canada-made Point Grey CMOS industrial camera (FL3-U3-13Y3M-C) with a China-made lens, a Texas instrument projector DLP LightCrafter 4500 EVM. The parameters that can be set in the process of system calibration are discussed in the paper, and the scanning results with parameter change are evaluated based on the indicators of camera and projector’s reprojection error, scanning resolution and point cloud’s uniformity. The research concludes that the distance between the projector and the calibration board is a key factor needs to be controlled. It can be set up properly based on the indicators for the quality of scanned data, which improves the speed of system calibration and keep the collected point cloud data more stable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".