3D SHAPE ACQUISITION USING AN FTP-BASED METHOD IN PRODUCT MODELING
Bibliographic record
Abstract
Three-dimensional (3D) shape modeling is one of the most fundamental processes in CAD/CAM systems. There is a variety of methods to build 3D shapes for product design and manufacturing. The methods include defining a 3D object using solid or feature modeling methods, or building a 3D shape of the existing object using reverse engineering techniques. Image-based shape recovery techniques provide effective tools in reverse engineering to acquire 3D data of objects. This paper reports a simple method to reconstruct a 3D object from its 2D (two-dimensional) image for the product modeling. A method based on FTP (Fourier Transform Profilometry) phase analysis is proposed to measure the 3D surface of an object. The comparison of the FTP method with other methods is discussed and the process of the FTP method is provided. The experiment shows the accuracy and speed of the method.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".