CONTOUR-BASED 3D POINT DATA SIMPLIFICATION FOR FREEFORM SURFACE RECONSTRUCTION
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
Three-dimensional clouds of largely unorganized coordinate data are often used to reconstruct freeform surfaces and shapes for a variety of seemingly diverse reverse engineering applications involving computer-aided design, anatomical reconstruction, cartography, digital archaeology, and infrastructural renewal. The point cloud data acquired by non-contact digitizers is very dense and includes numerous scanning errors. As a consequence, the captured data must be filtered and simplified for accurate surface reconstruction. Many existing data simplification techniques are, however, complex and do not directly support the development of spline-based surface models. In this paper a novel contour-based simplification algorithm is introduced for creating B-spline facial surface models directly from scanned data. The algorithm first extracts a series of equally-spaced sectioned contours from an unorganized 3D point cloud by mapping points onto a set of user-defined parallel planes. Each extracted contour is then regenerated as a cubic B-spline curve with a reduced number of control points using a user-defined reduction ratio. A freeform surface is finally created from these contiguous reconstructed contours by a lofting process. Deviation analysis that compares the final reconstructed surface to the original point cloud data is used to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm generates a fairly accurate spline-based surface model from unstructured points using less than 20% of the actual scanned data. Surface accuracies are enhanced with increased number of initial contours and a greater second stage data reduction ratio.
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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 | 0.001 | 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 | 0.001 |
| 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 it