Collision detection algorithm for NURBS surfaces in interactive applications
Bibliographic record
Abstract
Video games have reached a new level of realism. Programmable shading, 3D physics simulation and curved surfaces will soon become standard features. Real time collision detection, needed for this kind of application, is a difficult problem with no known optimal solution. We present a new algorithm for interactive collision detection between dynamic NURBS surfaces. It is intended to be used in real time applications, particularly in 3D video games embedded in an environment governed by simulated physical laws. This algorithm creates oriented bounding boxes (or OBB) on the fly with the surface control points and tests them for overlapping. If this test fails, the surfaces are subdivided into smaller NURBS surfaces and the algorithm is called recursively on these new surfaces. It stops when a certain precision level is reached, that is user definable as a function of the application. The results are the world space coordinates of the contact point, and the (u, v) parametric coordinates on both surfaces. The use of OBB allows for fast and memory efficient collision tests. The construction of an OBB with the surface's control points is simple and leads to a tight fitting bounding volume, which is the key of this fast collision detection algorithm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".