Automatic Method for Sharp Feature Extraction from 3D Data of Man-made Objects
Bibliographic record
Abstract
A novel algorithm is proposed for extracting sharp features automatically from scanned 3D data of man-made CAD-like objects. The input of our method consists of a mesh or an unstructured point cloud that is captured on the object surface. First, the vector between a given point and the centroid of its neighborhood at a given scale is projected on the normal vector and called the ‘projected distance’ at this point. This projected distance is calculated for every data point. In a second stage, Otsu's method is applied to the histogram of the projected distances in order to select the optimal threshold value, which is used to detect potential sharp features at a single scale. These two stages are applied iteratively with the other incremental scales. Finally, points recorded as potential features at every scale are marked as valid sharp features. The method has many advantages over existing methods such as intrinsic simplicity, automatic selection of threshold value, accurate and robust detection of sharp features on various objects. To demonstrate the robustness of the method, it is applied on both synthetic and real 3D data of point clouds and meshes with different noise levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".