Modeling Deformable Objects for Computer-Aided Sculpting (CAS)Modeling Deformable Objects for Computer-Aided Sculpting (CAS)
Bibliographic record
Abstract
Software tools for modeling complex freeform objects require the designer to use sophisticated procedures and complex protocols that do not inherently support the creative design process. Typical tasks include tedious control point manipulation and manual surface patch stitching operations. An interactive computer-aided sculpting (CAS) framework based upon deformable geometric models is described in this paper. The technique exploits the topology and learning algorithm of a self-organizing feature map (SOFM) to generate an adaptive volumetric mesh comprised of hexahedral elements. The pre-ordered lattice of the SOFM maintains the relative connectivity of neighbouring nodes in the mesh as it transforms under external and internal forces. Prior to virtual sculpting, the shape primitive is either retrieved from the object database or created by fitting a deformable mesh to representative surface points. Material and dynamic properties are incorporated into the deformable solid model by treating the surface and interior nodes as point masses connected with a network of springs. Illustrations are provided to demonstrate the virtual sculpting framework
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".