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State-of-the-art ray tracing techniques operate on hierarchical accelerationstructures such as BVH trees which wrap objects in a scene into boundingvolumes of decreasing sizes. Acceleration structures reduce the amount ofray-scene intersections that a ray has to perform to find the intersectingobject. However, we observe a large amount of redundancy when rays aretraversing these acceleration structures. While modern acceleration structuresexplore the spatial organization of the scene, they neglect similaritiesbetween rays that traverse the structures and thereby cause redundanttraversals. This paper provides a limit study of a new promising technique,Hash-Based Ray Path Prediction (HRPP), which exploits the similarity betweenrays to predict leaf nodes to avoid redundant acceleration structuretraversals. Our data shows that acceleration structure traversal consumes asignificant proportion of the ray tracing rendering time regardless of theplatform or the target image quality. Our study quantifies unused ray localityand evaluates the theoretical potential for improved ray traversal performancefor both coherent and seemingly incoherent rays. We show that HRPP is able toskip, on average, 40% of all hit-all traversal computations.

Hash-Based Ray Path Prediction: Skipping BVH Traversal Computation by Exploiting Ray Locality