One‐more‐vertex Next‐Event Estimation with Hierarchical Geometry Sampling

Abstract

Robust next‐event estimation (NEE) remains a challenge in scenes characterized by sparse or small‐scale geometry where indirect illumination is the primary transport mechanism. In these scenes, traditional path construction, which relies on local directional sampling, often fails to find intersections with the sparse geometry, and standard NEE also struggles as it typically connects vertices directly to emitters, failing when those connections are occluded or require intermediate bounces. We propose a novel approach that constructs paths via direct geometry sampling. Instead of relying on stochastic ray casting, we repurpose the scene's bounding volume hierarchy (BVH) as a hierarchical sampling structure. By performing a stochastic top‐down traversal, we transform the selection of the next path vertex into a hierarchical problem. To prioritize high‐throughput connections, the traversal is guided by a proxy contribution function evaluated at each internal node. This function leverages aggregated statistics of the geometry contained in the BVH nodes to efficiently estimate contribution during traversal. We demonstrate orders of magnitude improvements in complex scenarios such as indirect illumination from sparse geometry or rendering discrete scattering media.