Visualizing Query Traversals Over Bounding Volume Hierarchies Using Treemaps
Bibliographic record
Abstract
Bounding volume hierarchies (BVHs) are one of the most common spatial data structures in computer graphics. Visualizing ray intersections in these data structures is challenging due to the large number of queries in typical image rendering workloads, the spatial clutter induced by superimposing the tree in a 3D viewport, and the strong tendency of these queries to visit several tree leaves, all of which add a very high dimensionality to the data being visualized. We present a new technique for visualizing ray intersection traversals on BVHs over triangle meshes. Unlike previous approaches which display aggregate traversal costs using a heatmap over the rendered image, we display detailed traversal information about individual queries, using a 3D view of the mesh, a treemap of the BVH, and synchronized highlighting between the two views, along with a pixel grid to select a ray intersection query to view. We demonstrate how this technique elucidates traversal dynamics and tree construction properties, which makes it possible to easily spot algorithmic improvements in these two categories.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".