High-frequency color-encoded fringe-projection profilometry based on geometry constraint for large depth range
Bibliographic record
Abstract
In multi-view fringe projection profilometry (FPP), a limitation of geometry-constraint based approaches is the reduced measurement depth range often used to reduce the number of candidate points and increase the corresponding point selection reliability, when high-frequency fringe patterns are used. To extend the depth range, a new method of high-frequency fringe projection profilometry was developed by color encoding the projected fringe patterns to allow reliable candidate point selection even when six candidate points are in the measurement volume. The wrapped phase is directly retrieved using the intensity component of the hue-saturation-intensity (HSI) color space and complementary-hue is introduced to identify color codes for correct corresponding point selection. Mathematical analyses of the effect of color crosstalk on phase calculation and color code identification show that the phase calculation is independent of color crosstalk and that color crosstalk has little effect on color code identification. Experiments demonstrated that the new method can achieve high accuracy in 3D measurement over a large depth range and for isolated objects, using only two high-frequency color-encoded fringe patterns.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".