Distributed Out-of-Core Stochastic Progressive Photon Mapping

Abstract

At present, stochastic progressive photon mapping (SPPM) is one of the most comprehensive methods for a consistent global illumination computation. Even though the number of photons is unlimited due to their progressive nature, the scene size is still bound by the available main memory. In this paper, we present the first consistent out-of-core SPPM algorithm. In order to cope with large scenes, we automatically subdivide the geometry and parallelly trace photons and eye rays in a portal-based system, distributed across multiple machines in a commodity cluster. Moreover, modifications of the original SPPM method are introduced that keep both the utilization of tracer machines high and the network traffic low. Therefore, compared to a portal-based single machine setup, our distributed approach achieves a significant speedup. We compare a GPU-based with a CPU-based implementation and demonstrate our system in multiple large test scenes of up to 90 million triangles.At present, stochastic progressive Q21 photon mapping (SPPM) is one of the most comprehensive methods for a consistent global illumination computation. Even though the number of photons is unlimited due to its progressive nature, the scene size is still bound by the available main memory. In this paper, we present the first consistent out-of-core SPPM algorithm. In order to cope with large scenes, we automatically subdivide the geometry and parallelly trace photons and eye rays in a portal-based system, distributed across multiple machines in a commodity cluster. Moreover, modifications of the original SPPM method are introduced that keep both the utilization of tracer machines high and the network traffic low.