Saturation avoidance by adaptive fringe projection in phase-shifting 3D surface-shape measurement
Why this work is in the frame
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Bibliographic record
Abstract
Fringe-pattern projection systems are capable of non-contacting 3D surface full-field measurement with high accuracy. However, the systems are prone to intensity saturation and low signal-to-noise ratio (SNR) when measuring objects with a large range of reflectivity across the surface. Intensity saturation occurs when the light intensity directed to the camera exceeds the maximum intensity quantization level. A low SNR occurs when there is a low intensity modulation compared to the amount of noise in the image. Saturation and low SNR can result in significant measurement error. This paper presents a method for saturation avoidance during object-surface measurement, by adaptively adjusting the projected fringe-pattern intensities, through the maximum input gray level (MIGL). A high SNR can be maintained while avoiding saturation by combining the intensities from phase-shifted images captured at different MIGL, into a set of composite phase-shifted images. In measurement of a black and white checkerboard at different depths, the newly developed method reduced errors by an average 0.25 mm compared to the highest accuracy measurement using a uniform MIGL.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it