Robust continuous collision detection for interactive deformable surfaces
Bibliographic record
Abstract
Abstract Collision events between 3D objects in motion in computer animations or simulations are difficult to detect due to the difficulty of accurately sampling the motion paths of objects in space and time. One approach to this problem has been continuous collision detection but because the current approaches process potentially interacting primitive pairs (PIPPs) redundantly. This is time‐expensive, especially where there are a large number of PIPPs. In this paper we propose a novel collision detection process that more accurately and robustly detects collisions on simulated meshed deformable surfaces. We embed a new layer, primitive filtering layer (PFL), to extract PIPPs. This has two results. It reduces the number of PIPPs significantly and it means that each interacting primitive pair is processed just one time. Experimental results show that this approach achieves interactive rates for complex deformable surfaces with large contact regions. This is especially practical for cloth dynamics. Our method is efficient, accurate, reliable, and robust even in the presence of objects with sharp features. We also present techniques to implement the method on programmable graphics processing units (GPUs). Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".