Subdividing a 3D space into cells or voxels and using positional and/or directional information to directly index into it is a popular method to store and access local data, typically using 3D textures. This has been the basis of many global illumination algorithms, it is been used to store light lists, specular probes and decals…

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Explores using spatial hashing as a memory-efficient alternative to 3D textures for storing raytraced ambient occlusion data. The technique quantizes 3D space into cells indexed by hash values derived from world position and surface normals, enabling caching and reuse of RTAO calculations for static scenes. Implementation details include hash functions (pcg, xxhash32), collision resolution via linear probing, distance-based cell sizing, and age-based cache eviction. Results show significant performance improvements (0.4ms vs 1.72ms) with reduced noise and better stability compared to traditional RTAO, though limited to static geometry.

Spatial hashing for raytraced ambient occlusion