A low-cost three-dimensional reconstruction and monitoring system using digital fringe projection
Bibliographic record
Abstract
In this paper, the design and construction of a low-cost three-dimensional (3D) reconstruction and monitoring system using digital fringe projection (DFP) is proposed, which can perform small- and large-scale measurements in different environments and can be applied to various applications such as intelligent monitoring, 3D online inspection, and obstacle detection for vehicle guidance. The contribution of this paper is threefold: (i) development of a comprehensive 3D measuring system that performs sensors handling, coordinate acquisition, reconstruction, and display process simultaneously and quickly; (ii) proposal of new filters to improve quality and efficiency of the system; and (iii) development of a real-time virtual 3D measurement system to calibrate and analyze the proposed methodologies. Optical and simulation measurement results are presented to verify the feasibility and performance of the developed systems. The observed RMS difference was found to be <0.021 mm in the optical measurement. From measured results, it can be concluded that the proposed systems and adopted methodology are effective in obtaining 3D surface profiles.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".