Convex hull covering of polygonal scenes for accurate collision detection in games
Bibliographic record
Abstract
(a) A building model used in computer games. (b) Convex hull covering computed by our algorithm. Figure 1: A result of convex hull covering. (a) A complex building mesh used in games, where front and top walls are culled to reveal the interior structures. The building contains a disconnected collection of closed and open mesh pieces with highly non-uniform tessellations. (b) The convex hulls obtained, shown in different colors, collectively cover the building geometry (they may overlap, hence a covering), but do not take away any original game playing space — this is our accuracy requirement. The original model has 14,608 polygons and the algorithm returned 3,137 convex hulls. Although the convex hull count is still high due to the strict accuracy requirement, about 80 % of collision entity reduction (triangles to convex hulls) still provides great potential to lower the computation cost of collision detection. Decomposing a complex object into simpler pieces, e.g., convex patches or convex polyhedra, is a well-studied geometry problem. A well constructed decomposition can greatly accelerate collision detection since intersections with and between convex objects are fast to compute. In this paper, we look at a particular instance of the convex decomposition problem which arises from real-world game development. Given a collection of polyhedral surfaces (possibly
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".