Extracting of Cross Section Profiles from Complex Point Cloud Data Sets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Point cloud data sets are widely used in design and manufacturing. Extracting 2D and 3D features from a point cloud is a field that many researchers are working on. In the present work, a slicing algorithm is implemented for segmenting a point cloud by parallel planes in the X, Y and Z directions and storing the point coordinates within each sectioned plane into separate files. After slicing, a new method is developed for filtering and extracting the outer boundary for each cross section. In the next step, this algorithm is combined with the density-based spatial clustering of applications with noise (DBSCAN) method to achieve a better boundary extraction result for complex outlines. The codes are written in Python (v. 3.7) and executed in Spyder using the Anaconda software package (v. 5.3.1). Complex case studies (*.STL lung model and a femur model) are used to illustrate the merits of this approach.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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