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Lightweight attention-based indirect illumination

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SungYe Kim, Wojciech Uss, Wojciech Kaliński, Alexandr Kuznetsov, Rama Harihara, Harish Anand

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Lightweight attention-based indirect illumination is an AMD GPUOpen technical article summarizing a SIGGRAPH 2026 research direction for real-time indirect lighting. Authors SungYe Kim, Wojciech Uss, Wojciech Kaliński, Alexandr Kuznetsov, Rama Harihara, and Harish Anand describe a compact neural model that augments screen-space buffers with reflective shadow map data.

The method combines G-buffer features, depth, and normals with multi-head attention so the network can reason about off-screen surfaces and recover lighting effects that pure screen-space techniques miss. The write-up explains training constraints, runtime cost targets, and how the model fits into a forward rendering frame budget on Radeon hardware.

It assumes familiarity with real-time deferred or hybrid pipelines, shadow maps, and modern neural shading literature. Advanced rendering engineers evaluating learned indirect lighting for games or demos are the primary audience.

GPUOpen hosts the article with figures and links to related AMD rendering research. Material is free to read and pairs with other GPUOpen posts on neural materials and denoising.

GPUOpen SIGGRAPH summaries are widely read among PC graphics programmers tracking AMD's research-to-sdk pipeline, though independent practitioner reviews of this specific attention model are still emerging.