DOBB‐BVH: Efficient Ray Traversal by Transforming Wide BVHs into Oriented Bounding Box Trees using Discrete Rotations
Bibliographic record
Abstract
Abstract Oriented bounding box (OBB) bounding volume hierarchies offer a more precise fit than axis‐aligned bounding box hierarchies in scenarios with thin elongated and arbitrarily rotated geometry, enhancing intersection test performance in ray tracing. However, determining optimally oriented bounding boxes can be computationally expensive and have high memory requirements. Recent research has shown that pre‐built hierarchies can be efficiently converted to OBB hierarchies on the GPU in a bottom‐up pass, yielding significant ray tracing traversal improvements. In this paper, we introduce a novel OBB construction technique where all internal node children share a consistent OBB transform, chosen from a fixed set of discrete quantized rotations. This allows for efficient encoding and reduces the computational complexity of OBB transformations. We further extend our approach to hierarchies with multiple children per node by leveraging Discrete Orientation Polytopes (k‐DOPs), demonstrating improvements in traversal performance while limiting the build time impact for real‐time applications. Our method is applied as a post‐processing step, integrating seamlessly into existing hierarchy construction pipelines. Despite a 12.6% increase in build time, our experimental results demonstrate an average improvement of 18.5% in primary, 32.4% in secondary rays, and maximum gain of 65% in ray intersection performance, highlighting its potential for advancing real‐time applications.
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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 | 0.002 |
| 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) | 0.004 | 0.002 |
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".