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Real-time global illumination that scales from low to high-end hardware is important for interactive applications so they can reach wider audiences. To do this, the real-time lighting algorithm used needs to have varying performance characteristics.  Sparse Radiance Probes (SRP) is a recent real-time global illumination algorithm that runs in under 5 ms per frame on a high-end Nvidia Titan X GPU. Its low per-frame timings suggest it could scale to low-end devices, but no prior work provides complete implementation details and evaluates its performance across devices with varying performance characteristics to prove this. Therefore, this thesis aims to fill this gap and determine if SRP is scalable across low to high-end devices. SRP is implemented with adjustable scaling parameters, and its performance is compared across three test devices. A low-end iPhone 7, a mid-range AMD Radeon 560 graphics card, and a high-end AMD RX Vega 56 graphics card. The implementation in this thesis ran above 60 FPS for simple scenes on the iPhone 7, and with a reasonable reduction in quality, it ran just above 30 FPS on more complex scenes like Crytek Sponza. These results show that SRP can scale to low-end devices. While the implementation in this thesis runs in real time, there are implementation optimisations that would make SRP run even faster across all the test devices without reducing quality.

Global Illumination on a Mobile Phone: Scalable Real-time Global Illumination using Sparse Radiance Probes