Contour-based 3D point cloud simplification for modeling freeform surfaces
Bibliographic record
Abstract
The reconstruction of accurate freeform surface models of 3D scanned objects is a common task encountered in creating virtual reality environments for anatomical reconstruction, cartography, cultural artifact modeling, digital archaeology, infrastructure renewal, and computer-aided design. Difficulties occur in reconstructing smooth surfaces from these scanned data sets because the acquired data is very large and is often infiltrated with scanning errors. For surface reconstruction, visualization, and interactive virtual modeling, it is necessary to reduce the amount of raw scanned data. Many existing data simplification techniques are complex and not directly applicable to spline-based surface models. A novel two stage contour-based data simplification algorithm is introduced in this paper and applied to facial surface reconstruction, which may be used for human modeling for computer games or model creation for virtual museums. The first stage extracts a series of equally spaced sectioned contours directly from a dense 3D data points. In the second stage, each extracted contour is redefined as a cubic B-spline curve by reduced number of control points defined by a user defined reduction ratio. A lofted surface is finally created from these reconstructed contours. The effectiveness of the synthetic surface reconstruction algorithm is demonstrated using its deviation values from its original point cloud data set. The experimental results show that the proposed algorithm generates a fairly accurate spline based facial model with only 5-20% of the actual scanned data, based upon the surface complexity. Performance can be improved by increasing the number of extracted contours, followed by a greater reduction ratio in the second data simplification stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".