Cloud data segmentation and classification for reverse engineering using neural networks
Bibliographic record
Abstract
Automatic segmentation of point data in the past has been mainly applied to single range maps. However, there is a great need for the segmentation of fully digitized objects with multiple viewpoints. This research reports on the automatic segmentation of multiple viewpoint 3D digitized data captured by a laser scanner or a CMM. This is accomplished in two steps. Firstly, the surface normal and principal curvatures are estimated at corresponding point locations. Local Darboux frame and weighted least-square surface fitting are used to calculate the normal values and curvature values of the point data. Secondly, an eight dimensional feature vector (3D coordinate, 3D normal, Gaussian and Mean curvature) is used as an input to a Self-Organized Feature Map (SOFM). A normalized feature vector and a weighted Euclidean distance are adopted in the learning process of the SOFM, which improves the speed and exactness of the segmentation. The segmentation using SOFM is robust to noise and has no limitation to surface type. The algorithm is validated by real and synthetic point data. To improve the quality of surface fitting, segmented subregions of typical surfaces are classified by using a back propagation neural network. The techniques developed play a key role in reducing the length of product development time and the quality of a final surface model.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".