Two-step triangular phase-shifting method for 3-D object-shape measurement
Bibliographic record
Abstract
Traditional sinusoidal phase-shifting algorithms involve the calculation of an arctangent function to obtain the phase, which results in slow measurement speed. This paper presents a novel high-speed two-step triangular phase-shifting approach for 3-D object measurement. In the proposed method, a triangular gray-level-coded pattern is used for the projection. Only two triangular patterns, which are phase-shifted by 180 degrees or half of the pitch, are needed to reconstruct the 3-D object. A triangular-shape intensity-ratio distribution is obtained by calculation of the two captured triangular patterns. Removing the triangular shape of the intensity ratio over each pattern pitch generates a wrapped intensity-ratio distribution. The unwrapped intensity-ratio distribution is obtained by removing the discontinuity of the wrapped image with a modified unwrapping method commonly used in the sinusoidal phase-shifting method. An intensity ratio-to-height conversion algorithm, which is based on the traditional phase-to-height conversion algorithm in the sinusoidal phase-shifting method, is used to reconstruct the 3-D surface coordinates of the object. Compared with the sinusoidal and trapezoidal phase shifting methods, the processing speed is faster with similar resolution. This method therefore has the potential for real-time 3-D object measurement. This has applications in inspection tasks, mobile-robot navigation and 3-D surface modeling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".