DOBB‐BVH: Efficient Ray Traversal by Transforming Wide BVHs into Oriented Bounding Box Trees using Discrete Rotations
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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