Improving 3D reconstruction accuracy in wavelet transform profilometry by reducing shadow effects
Bibliographic record
Abstract
Wavelet transform profilometry is a three‐dimensional (3D) reconstruction method based on the structured light technique of fringe pattern projection, widely used because it is a non‐invasive, high‐performance 3D reconstruction method. The presence of shadows created by the object in the image capture process is an obstacle in obtaining accurate 3D reconstructions, as they add noise to the phase data, leading to artefacts in object reconstruction, even when using robust phase‐unwrapping algorithms. Since shadows present diverse intensities and shapes, detecting and eliminating their effects are challenging tasks. This work presents a novel method to detect shadow regions and reduce their effects in 3D reconstruction. The proposed method uses coloured fringe patterns to detect the shadows and mathematical morphology to condition the outlines of the shadow regions. The shadow outline information is used to interpolate the background‐plane fringe pattern onto the captured scene, where the shadows are detected. The mean squared error (MSE) of the reconstructed objects is reduced to 25% of the MSE without shadow removal, on an average, when using the Bioucas phase‐unwrapping method. When using the Ghiglia phase‐unwrapping method, the MSE reduction is to 8.3%, on an average, of the MSE in the shadow case.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".