Découpage virtuel interactif de corps élastiques pour simulation chirurgicale
Bibliographic record
Abstract
Soft body deformation models are commonly used in surgery simulations.However, cutting those models can have a severe impact on computation times and affects the interactivity of the simulation.We propose a novel method for modeling topology and introducing cuts in a meshless soft body simulated on a background grid, as well [as] a way to progressively update the visual aspect of the object by adding a small number of triangles to the surface mesh to cover the cut area.We determine that the accuracy of the deformation is preserved after cutting by comparing our method to a finite element method.Tests show that this new method achieves interactive simulation rates with more than 10000 elements while cutting the model and reconstructing the mesh.Our separation of the visual and physical aspects of the simulation allows for more flexibility when tuning the performance of the simulation.Topology modifications have little impact on computation times for either physical or visual changes.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".