Convex hull covering of polygonal scenes for accurate collision detection in games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it