Bibliographic record
Abstract
Even though many games feature complex 3D environments, 3D pathfinding remains a challenging problem. Representing large 3D maps can require a lot of memory, and pathfinding instances must be solved very quickly while the game is running. In this work we develop an efficient solution to 3D pathfinding by building a reduced, hierarchical grid representation within which we can extend traditional 2D navigation mesh (navmesh) pathing. Starting from an octree representation, we merge adjacent cells while preserving their convexity to obtain a coarser representation that greatly reduces path computation costs. We then build a navigation graph from this octree within which we can search for paths using the popular A* search algorithm. To increase the quality of the paths we obtain we implemented two forms of path refinement: a visibilitybased path pruning heuristic, and a 3D extension of the classic “funnel” algorithm that computes minimal homotopic paths. We further extend our work to handle dynamic environments with local and efficient updates to the octree and the movement graph. Experiments on a variety of scenarios show that our approach remains fast and efficient even for very large 3D maps and could be used for real-time pathfinding in video games. A detailed comparison with the state-of-the-art JPS-3D algorithm shows that our approach produces shorter path lengths while being faster on long path instances. We implemented our work in Unity, one of the most popular game engines, as an effort to make pathfinding in 3D environments accessible to game developers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".